literature review of pneumatic system

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• Corpus ID: 44232383

A Review of Pneumatic Actuators (Modeling and Control)

• H. Ali , S. Bahari , +2 authors M. Marhaban
• Published 2009
• Engineering

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90 Citations

Dynamic behaviour of pneumatic actuators in open-loop controlled by proportional valves, design and control of the three position pneumatic cylinder actuatorfor precision positioning under vertical loading, positioning of pneumatic actuator using open-loop system, non-linear modeling and cascade control of an industrial pneumatic actuator system, sliding mode control for a pneumatic servo system with friction compensation, incremental nonlinear dynamic inversion control of long-stroke pneumatic actuators, modeling and controller design of pneumatic actuator system with control valve, tracking control of pneumatic muscle using lyapunov based control method, design of combined robust controller for a pneumatic servo actuator system with uncertainty, parameter tuning process for a closed-loop pneumatic actuator, 67 references, non-linear pressure observer design for pneumatic actuators, dynamic pneumatic actuator model for a model-based torque controller, improved control of a pneumatic actuator pulsed with pwm, fuzzy state feedback gain scheduling control of servo-pneumatic actuators, an intelligent control for state-dependent nonlinear actuator and its application to pneumatic servo system, dynamic analysis of pneumatic actuators, nonlinear modeling and control of servo pneumatic actuators, practical robust control design of pneumatic servo systems, robust control of pneumatic actuators based on dynamic impedance matching, modelling a loaded two-way pneumatic actuator, related papers.

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• Published: 28 November 2023

Comparison of hydraulic, pneumatic and electric linear actuation systems

• Jan Pustavrh 1 ,
• Marko Hočevar 1 ,
• Primož Podržaj 1 ,
• Ana Trajkovski 1 &
• Franc Majdič 1  

Scientific Reports volume  13 , Article number:  20938 ( 2023 ) Cite this article

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• Engineering
• Mechanical engineering

Different applications or industries use different systems for linear actuation, such as hydraulic, pneumatic or electric. Electric systems are becoming increasingly popular and are already replacing hydraulic systems in various applications. These are known to be potentially harmful to the environment, as large amounts of fluid can be released into the environment in the event of a pipe burst or other accident. This paper presents the results of a comparison between hydraulic, pneumatic and electric systems under variable conditions but with similar loads in all three systems. The common feature of all three systems is the input power, which was limited to 1.1 kW. There was no hydraulic accumulator in the hydraulic system and no pressure vessel in the pneumatic system, so no stored energy could influence the system behaviour or results. The main difference between the systems studied was the profile of displacement and power consumption. The most consistent response and lowest power consumption were obtained with the electric system, although both hydraulic and pneumatic systems can achieve consistent response with some modifications.

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Introduction.

Hydraulic systems are commonly known to be potentially dangerous, as oil can leak into the environment in the event of a simple pipe burst or other accident or malfunction of the system 1 . In industry, hydraulic systems are increasingly being replaced by partial or complete electrification (aircraft, off-highway machinery, commercial vehicles, etc.) in order to reduce the impact on the environment, but on the other hand, hydraulic systems are still used in many industries 2 , 3 , 4 , 5 , 6 . Numerous studies are being carried out with the aim of replacing hydraulic fluids that are harmful to the environment with those that are compatible with nature and humans 1 , 7 , 8 , 9 , 10 , 11 . Pneumatic and electric systems are much more environmentally friendly as they have no direct impact on the environment. The latter systems are now being used more and more, and their capabilities are improving. A previous study 2 , 3 , compared and evaluated the characteristics of different servo motors using catalogues and non-public data, and another study 12 compared three drive technologies (hydraulic, pneumatic and electric) for the same load positioning task. In study 13 , a simulation tool was used to compare the suitability of the choice between a hydraulic and an electric actuator on the Stewart platform. However, to our knowledge, comparable studies and experimental evaluations with hydraulic, pneumatic and electric linear actuators have not yet been carried out.

This paper presents a comparison of hydraulic, pneumatic and electric systems with linear actuators. Each system has its own characteristics and advantages that make it suitable for different applications. Hydraulic systems have become popular in industry because of their high force-torque ratio 3 , 14 , 15 , 16 . Actuators derive their energy from pressurised oil acting on the piston, which generates a force that sets the rod in motion 17 . They are usually executive components in hydraulic systems 18 , 19 . They are used in industry and manufacturing, as well as in heavy machinery and construction equipment (excavators, bulldozers, forklifts, telescopic ladders, etc.).

Pneumatic systems work on the same principle as hydraulic systems, with the difference that they use compressed air instead of oil 20 . Pneumatics are often used in automation technology, in applications that require a clean and dry environment. Recently, soft actuators have been increasingly used in automation systems to grip fragile products or semi-finished products 21 .

Electric actuators convert electrical energy (power) into mechanical energy or the rotary motion of a motor (servo or other) into linear motion via a screw and bearing system. They are used in a wide range of applications, from small domestic appliances to large industrial machinery. Their precision and control make them suitable for applications where precise and repeatable movement is required 20 , 22 .

Hydraulic 2 , 6 , 14 , 16 , 18 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 and pneumatic 20 , 21 , 26 , 33 , 34 , 35 , 36 systems are quite easy to compare because both systems convert the energy stored in the fluid into mechanical energy (force or torque) and the principle of operation is almost identical. The only difference is that the most common hydraulic systems usually use oil as the fluid, while pneumatic systems work with air. These two fluids have quite different properties (compressibility, density, etc.). It is much more difficult to compare an electric 2 , 9 , 14 , 20 , 22 , 37 , 38 system with a hydraulic or pneumatic system, because the only commonality is the input power (power consumption) or the output force they produce. In this study, the limit was the input power (1.1 kW) without stored energy (hydraulic accumulator or pressure vessel).

Experimental setup

In order to be able to compare the hydraulic, pneumatic and electrical systems, the electrical input power was limited to 1.1 kW. The experimental part of each system is performed on a table with linear guides (Fig.  1 a, b). It consists of the fixed part used for each actuator type (hydraulic, pneumatic, and electric), i.e. slides on linear guides on which we can place different masses (Fig.  1 b) to vary the inertial mass (simulation of different loads), a linear displacement transducer (MAX48N-12V10EE0500, SICK), and a load cell (1-U2AD1/500 KG, HBM). The LabVIEW software environment with the National Instruments CompactRIO system was used for control and data acquisition (1000 Hz sampling rate). The individual system with actuator, control and measuring system is presented below.

( a ) Without load and ( b ) with additional load.

The experiment is performed by applying a stepping function to the valve or servo motor of the electric actuator without a feedback loop. During the experiment, the piston rod is not allowed to reach its final or initial position (without bumping) when moving back, and the mass of the load is varied during the experiment (0 kg and 50 kg). In all three systems, the current consumption was measured with a 3-phase current sensor (AS050- SD -10V-3P, Loulensy Inc.).

In order to be able to compare all three systems and thus the response, it was necessary to define variable input data. For the hydraulic and pneumatic system this was the pressure at the pressure relief valve, for the electrical system, the maximum permissible acceleration was defined.

In the hydraulic system, the pressure relief valve was set to 50 bar, 100 bar and 200 bar; in the pneumatic system to 6 bar and 8 bar, and in the electrical system, the maximum permissible acceleration was set to 0.1 m/s 2 , 5 m/s 2 , 10 m/s 2 and the maximum allowable value of the system to 15 m/s 2 .

Hydraulic system

The schematic of the hydraulic system is shown in Fig.  2 a. The hydraulic system (Fig.  2 b) consists of a double-acting hydraulic actuator (1) ϕ25/16 × 200, position 2 is a 4/3 proportional directional control valve (D1FPE01MC9NS0047, Parker), the hydraulic power unit (position 3) consists of a 1.1 kW electric motor (3.3) driving a gear pump (3.2) with a displacement of 3.6 cm 3 /rev (PGP502A0036CV1P1NE1B1E1B1, Parker), a pressure relief valve (3.1), a check valve (3.4), a high-pressure filter (4) and a return filter (5) to ensure cleanliness of the oil. Positions 6.1, 6.2, 6.3 and 6.4 are pressure sensors (SCP-400-74-02, Parker), position 7 is a linear position sensor (MAX48N-12V10EE0500, SICK), position 8 is a load cell (1 - U2AD1/500 KG, HBM), position 9 is a flow sensor (SCFT-060-02-02, Parker) and position 10 is a temperature sensor (SCT -190–04-02, Parker).

( a ) Hydraulic system scheme and ( b ) actual hydraulic system.

Pneumatic system

The pneumatic scheme is shown in Fig.  3 . A double-acting actuator (1) ϕ63/20 × 150 (DNC-63-160-PPV-A, Festo) controlled by a 5/3 proportional directional control valve (2, MPYE-5-1/8-HF-010-B, Festo) was used. Position 3 is a 1.1 kW compressor for air supply without pressure vessel. Position 4 is a pressure relief valve, positions 5.1, 5.2 and 5.3 are pressure sensors (SCP-015-74-02, Parker), 6 is a linear position sensor (MAX48N-12V10EE0500, SICK), 7 is a load cell (1-U2AD1/500 KG, HBM), 8 is a flow sensor (PFM711-C8-F, SMC) and position 9 is a temperature sensor (SCT -190–04-02, Parker).

Pneumatic system scheme ( a ) and actual pneumatic system ( b ).

Electrical system

The electrical circuit diagram is shown in Fig.  4 a. The electrical system (Fig.  4 b) consists of an electric actuator (1, ESBF-BS -63-100-10P-S1-R3-F1, Festo). The servo motor (2, EMME-AS -80-S- HS-AMB, Festo) drives the lead screw, which converts the rotational movement into a translational movement. Position 3 is the servo drive controller (CMMT-AS -C2-3A- MP -S1, Festo), 4 is the position sensor (MAX48N-12V10EE0500, SICK) and 5 is the load cell (1-U2AD1/500KG, HBM). Unlike hydraulic or pneumatic systems, you only need cables for power transmission.

( a ) Electric system scheme and ( b ) a photo of actual electric system 39 .

The results of the measurements on the individual systems are presented below.

Figure  5 shows the results of the measurements on the hydraulic system with a system pressure of 50 bar, 100 bar and 200 bar.

Charts of hydraulic measurements; ( a ) displacement, ( b ) force, ( c ) velocity, ( d ) hydraulic power, ( e ) acceleration and ( f ) electric power verses time.

Since velocity is the first derivative and acceleration the second derivative of displacement, the deviations are clearly visible in both Fig.  5 a, c and e. The maximum deviation from the expected (linear) value occurs at a system pressure of 200 bar in the unloaded state, with the highest velocity and acceleration and the lowest velocity and acceleration occurring at a system pressure of 50 bar in the unloaded state.

Figure  5 c shows curves representing the measurements with and without load, and the measurements with load differ significantly. The oscillation frequency is significantly higher in the unloaded measurements than in the loaded measurements. The unloaded carriage settles after one period at constant velocity (0.2 m/s), while the period of oscillation of the loaded carriage decreases towards constant velocity (0.2 m/s) due to the influence of the inertial mass (acceleration and deceleration). At a system pressure of 200 bar, where this phenomenon is most pronounced, the period of oscillation is 0.15 s for the unloaded measurement. However, when the platform is loaded, three periods of oscillation occur, with the first period lasting 0.51 s, the second 0.17 s (about 35% of the first period) and the last period 0.16 s (about 32% of the first period). When the piston rod of the hydraulic cylinder moves towards the starting point, a similar behaviour as described above occurs. The loaded measurements show a lower oscillation frequency than the unloaded measurements, where the oscillation frequency decreases up to a constant velocity of 0.3 m/s. The backward velocity is higher due to the differential hydraulic cylinder (smaller surface area for the same flow). All this can also be seen in Fig.  5 e (acceleration curves).

In Fig.  5 b it can be seen that at the beginning of the movement of the piston rod there is a compressive force because the piston rod "pushes" the carriage away from itself, but soon the compressive force turns into a tensile force and the "belly" can be clearly seen in Fig.  5 a. This means that the piston rod stops for a moment and the accelerated inertial mass wants to continue moving in the direction of the initial movement. For unloaded measurements, the compressive or tensile force is quite small, but increases as the system pressure increases. For measurements with load, the initial compressive force is much higher (20%) because the piston rod of the hydraulic cylinder has to push the load away from itself in addition to all the expected friction (seals, friction in the piping, etc.). After the initial compressive force, a higher tensile force occurs due to the greater inertia, but at the same time there is an oscillation between the tensile and compressive forces, with the frequency of the oscillation decreasing until the proportional valve is switched and the piston rod moves towards the starting point. During the measurements with the load, there is a sharp increase in the tensile force, which is due to inertia. At this point, the valve switches to the position where the piston rod starts to move back to the starting point, but the inertial mass continues to move in the other direction for a few moments. When approaching the starting point, the inertial mass "bumps" again, which is shown by the oscillation or transition between compressive and tensile force (the oscillation frequency decreases in the direction of constant friction).

The hydraulic power (Fig.  5 f) depends on the flow rate and the pressure. When the valve is switched, the pressure drops to the value required to overcome pressure losses, friction, etc., and at this point flow enters the system. The oil begins to fill one chamber of the hydraulic cylinder and flows out of the other through the valve into the reservoir. At the beginning of the movement, the hydraulic power is "zero", which is mainly due to the energy or potential energy stored in the lines, even if the hydraulic accumulators have not been used. When the system pressure is increased, the hydraulic power also increases. In unloaded measurements, the hydraulic power was constant when the piston rod moved from the starting position (20 W), the increase only occurs at the beginning of the movement, but is very significant at a system pressure of 200 bar. Another immediate increase occurs when the valve is switched, when there is a small increase in pressure. When measuring with the load, the potential energy or pressure difference is so great that the hydraulic power is zero. This phenomenon is best observed at a system pressure of 200 bar, where the pressure difference is greatest. In the unloaded measurements, the power increase occurs after approx. 0.1 s, followed by a short increase and constant power; in the loaded measurements, the power increase only occurs after approx. 0.5 s. Similar to the unloaded measurements, there is a short power increase, after which the oscillation frequency drops to a constant power. As can be seen in Fig.  5 f, when the system pressure is increased, the electric motor is loaded more before the valve is switched from the zero position, because the hydraulic pump generates a higher pressure. At a system pressure of 200 bar, the electrical power was about 1500 W before the piston rod started to move, while at 50 bar the electrical power was more than twice as much, i.e. about 740 W. After the valve was switched and the piston rod of the hydraulic cylinder started to move, the power dropped to about 650 W.

Figure  6 shows the results of the measurement with a pneumatic system. The system pressure was set at 6 bar and 8 bar. With increasing system pressure, a similar displacement of the piston rod of the pneumatic cylinder was obtained. Figure  6 a shows the influence of the compressibility of the air, especially for measurements with load.

Charts of pneumatic measurements; ( a ) displacement, ( b ) force, ( c ) velocity, ( d ) hydraulic power, ( e ) acceleration and ( f ) electric power verses time.

The results of using the pneumatic system were surprising, as it was expected that the compressor would not be able to supply enough air because the reservoir (pressure vessel) was not used.

Figure  6 c shows that the pneumatic system behaved better when the measurements were made with load. After switching the valve, the velocity increased to a constant value (0.35 m/s), while in the measurements without load, the velocity oscillated to the maximum value until the valve was switched. When the piston rod moved towards the starting point, an oscillation was detected in the unloaded measurements, which could be due to the compressibility of the air or to a lack of air. The lack of air can be seen in Fig.  6 e, where the piston rod of the pneumatic cylinder accelerates and then cannot follow the movement due to the lack of air, resulting in oscillation. In the measurements with load, the velocity increased slowly (lower slope of the curve). Due to the slower acceleration at the beginning of the movement towards the end position, there was no sign of air shortage or oscillation, but the velocity was higher (0.6 m/s) than in the measurements without load, which is due to the inertial mass. When the valve was switched to the zero position, the effect of compressibility can be clearly seen in Fig.  6 c and e, as the oscillation of the carriage with the inertial mass at the end can be seen.

In Fig.  6 b, the compressive force is very low (− 20 N) in unloaded measurements where the piston rod has moved from its initial position, as the piston rod pushes the carriage away from itself. After switching the valve, there was an immediate increase in the tensile force, as the carriage continued to move in the direction of the initial movement for a few moments, after which the tensile force was very low again. In the measurements with load, the compressive force was ten times higher than in the unloaded measurements, but after the initial increase at the beginning, it decreased to very low values, very similar to those of the unloaded measurements. Due to the inertia of the mass, which continued to move in the direction of the initial position for a short time after the valve was switched (the piston rod began to move in the direction of the initial position), the tensile force increased sharply (480.5 N). The influence of the compressibility of the air is clearly noticeable in the measurements under load when the valve is switched to the zero position, as the movement of the inertial mass causes an oscillation between the compressive and tensile force (damped oscillation phenomenon, in which the oscillation frequency decreases towards zero with time).

Pneumatic power (Fig.  6 d) is related to flow and pressure. In unloaded measurements, when the valve is switched, the pressure drops to the value required to overcome the friction in the guides, seals, etc. Therefore, the pneumatic power is quite constant (70 W), the increase only occurs when switching the valve due to a short pressure rise. With loaded measurements, the values of the pneumatic power are greater, because, in addition to the friction in guides, seals, etc., additional pressure is required to push or pull the carriage with weights.

The values of the electrical power (Fig.  6 f) after the measurements with or without load do not differ, only with movement the power decreases. From this it can be concluded that the system is not overloaded.

Electric system

Figure  7 shows the results of the measurements on the electrical system.

Charts of electric measurements; ( a ) displacement, ( b ) force, ( c ) velocity, ( d ) electric power, ( e ) acceleration and ( f ) electric power on controller time.

At the limiting acceleration of 0.1 m/s 2 , a soft start can be seen in Fig.  7 a. The piston rod of the electric cylinder begins to move slowly to the end position, while it reacts more jerkily when the limit acceleration is increased. Increasing the limit acceleration causes a similar displacement of the piston rod or the threaded spindle of the electric cylinder. Unlike the similar displacement, the permissible acceleration 0.1 m/s 2 required 3.5 s to travel from the starting position to the end position and back without bumping. With the maximum permissible acceleration of 5 m/s 2 and 10 m/s 2 , the extension and retraction was five times shorter and with the maximum permissible acceleration of 15 m/s 2 almost six times shorter.

In addition to acceleration, the electrical system is also limited by velocity, which is about 0.3 m/s. This limitation can be seen in Fig.  7 c. With a permissible acceleration of 0.1 m/s 2 , the maximum velocity was not reached because the piston rod or spindle accelerated so slowly and evenly that the maximum velocity was not reached. The maximum velocity was 0.1 m/s. Figure  7 e shows that the measured values are within the configurable values, at the maximum permissible acceleration of 15 m/s 2 the acceleration was not exceeded.

At a maximum permissible acceleration of 0.1 m/s 2 as shown in Fig.  7 a, c and 7e see also Fig.  7 b), the acceleration was so smooth and slow that the force value is very low (from − 10 to 16 N). A different behaviour is visible when the maximum permissible acceleration is increased, especially for measurements under load. In unloaded measurements, a low compressive force (35 N) occurs initially, because the piston rod of the electric cylinder pushes the carriage out of itself, but at the same time friction in the guides, friction on the spindle in the electric cylinder etc. must be overcome. In the direction of the end position, the compressive force is very low. From the end position to the initial position, the piston rod pulls the carriage behind it, creating a tensile force. The initial tensile force is somewhat higher (35–45 N), but after the initial pull, the values drop to about 13 N. When the movement stops (deceleration), the carriage "bumps" against the piston rod, creating a compressive force. When measuring with the load, much greater compressive and tensile forces occur. The compressive force increases with the increase of the maximum permissible acceleration. Due to the inertia of the mass and the inability to follow the acceleration or movement of the inertial mass, there is an oscillation between the compressive and tensile forces (damped oscillation phenomenon). When the movement starts in the direction of the starting point, a high tensile force occurs due to the short movement of the inertial mass in the other direction. Here, too, an oscillation between the compressive and tensile forces occurs, because the inertial mass wants to move faster than the system allows due to the acceleration and "bumps" against the piston rod of the electric cylinder.

Figure  7 d shows that the power increases with the increase of the maximum permissible acceleration, whereby the load has an additional influence on the power increase.

A similar effect can be observed with power measurements directly at the controller (Fig.  7 f).

Comparison of hydraulic, pneumatic and electric actuators

The displacement curves of all three systems are shown in Fig.  8 . Some curves deviate from the expected linear displacement ("belly" occurs). Since the first derivative of displacement is velocity and the second is acceleration, the same phenomenon can be is seen in Figs.  5 , 6 and 7 c and e. In the hydraulic system, this phenomenon is particularly evident due to the large pressure difference Δp. A pressure of 2 bar is required for the forward movement of the piston rod, and about 10 bar for the backward movement, measured with or without load. Increasing the system pressure (50 bar, 100 bar or 200 bar) leads to a significant increase in Δp. The increase in pressure difference affects the velocity and acceleration. When the valve is switched, all the potential energy stored in the pipe is released and the velocity immediately increases significantly (e.g. at a system pressure of 200 bar without load 0.75 m/s or 0.67 m/s for the measurements with load), but when the system settles, the velocity is constant (0.18 m/s). The fact that the velocities are higher at the beginning than in the pneumatic or electric system can be seen in Fig.  8 , as the deviating curves have a steeper slope than the other curves. The increase in system pressure also affects the time it takes for the velocity to settle to the constant values (0.18 m/s). At 200 bar system pressure, it is 0.2 s without load and 0.9 s with load or just before the point where the valve is switched, i.e. at a time of 1 s. A similar oscillation occurs when the piston rod moves to the starting point, except that the pressure difference there is not as great as at the beginning of the movement (forward) when the valve was switched. The velocities are slightly higher (0.3 m/s) when the piston rod moves to the starting point, which is due to the smaller piston area and the same flow rate.

Displacement curves of all three systems.

In the pneumatic system, the pressure drops from the system pressure (i.e. 6 bar or 8 bar) to about 1 bar at the beginning of the piston rod movement, and the values do not increase significantly as the piston rod moves towards the starting point. When the valve was switched at the beginning, the velocity or acceleration does not increase as much as in the hydraulic system because of the much smaller pressure difference. Figure  6 c shows that the velocity increases to a constant value (0.37 m/s) when measured with load, while an oscillation is observed when measured without load. It is likely that the compressibility of the air causes the system to oscillate, as the frictional force in the guides increases during movement. A similar phenomenon occurs when the valve is switched or when the piston rod starts to move towards the starting point. Due to the smaller piston area (annular), higher velocities also occur in the pneumatic system. The velocity is higher in the measurement with load (0.65 m/s) than in the measurement without load (0.37 m/s), which is due to the influence of the mass inertia. In the measurements with load, when the valve is switched to the zero position at the end, the influence of the compressibility of the air can be seen, as a damped oscillation occurs. The velocity of the piston rod, to which the carriage with the additional mass is attached, decreases towards zero with time. Oil is also compressible (compressibility factor of oil 1.7 * 10 9  Pa), but air is much more compressible (compressibility factor of air 1.4 * 10 6  Pa) 40 .

The electrical system has no problem with the pressure difference or potential in the pipeline, because the system consumes as much energy (electricity) as it needs at that moment, whereas in the hydraulic or pneumatic system all the potential energy stored in the pipeline is released in the system when the valve is switched. During the measurements under load, some oscillations occurred, but the velocity quickly settled to a constant value of 0.3 m/s. In the hydraulic system, the energy peak is very noticeable (deviation from the expected linear movement of the hydraulic cylinder’s piston rod).

The velocity of each system can be determined from the slopes of the curves in Fig.  8 ; the steeper the slope, the higher the velocity. The curves of the hydraulic system have the greatest slope at the beginning, so that the velocity is the highest (0.76 m/s), and later the slope is the lowest, so that the constant velocity is the lowest (0.18 m/s). The pneumatic and electric systems have quite similar curve slopes, but the curves of the pneumatic system are slightly steeper, so that the velocity is slightly higher (0.37 m/s) than that of the electric system (0.3 m/s). Due to the change in piston area in the hydraulic and pneumatic systems, the return velocities are slightly higher. The slopes of the curves of the hydraulic and electric systems are quite similar, which is why the velocities are around 0.3 m/s. The pneumatic system has a slightly higher slope, and reaches a maximum velocity of 0.65 m/s during retraction.

Hydraulic and pneumatic systems are easier to compare because of similar characteristics (both convert the energy of a fluid into mechanical energy), while electrical systems convert electrical energy into mechanical energy. In all three systems, the influence of the inertial mass is evident in acceleration and deceleration (especially in the measurements under load). At the beginning, much higher compressive forces occur in the hydraulic system (− 530.9 N) than in the pneumatic (− 205 N) or electric system (− 413.1 N) due to the higher pressure differences. At the point where the valve has switched and the piston rods move backwards, but the inertial mass still wants to move in the other direction, very similar tensile forces occur (hydraulic 534.6 N, pneumatic 480.5 N and electric 459.5 N). The deviation in the hydraulic system may be due to the pressure shock, which causes a higher instantaneous acceleration and thus a higher tensile force. Oil and air are compressible, but when the piston rod of the pneumatic cylinder stops, there is much more oscillation than in the hydraulic system; in the electric system there is also oscillation, which can be caused by the factory-set control.

Figure  7 d and f show that power increases from zero to a certain value. For example, the maximum power at the maximum permissible acceleration of 15 m/s 2 was 233.6 W for the measurements without load and 313.9 W for the measurements with load. The power increases as the maximum permissible acceleration increases, and the system consumes more power for the measurements with load. In hydraulic (Fig.  5 f) and pneumatic (Fig.  6 f) systems, the power decreases or increases due to the continuous operation of the hydraulic power unit or compressor compared to the power required for continuous operation. In a hydraulic system, the power depends on the pressure setting of the pressure relief valve, as the electric motor is loaded more, similar to a pneumatic system, except that the difference between the pressures in the system is much smaller. The hydraulic system required 740.5 W of power at a system pressure of 50 bar before activation and 1529.9 W at 200 bar. After activation, the power dropped to about 650 W, indicating that a load of 50 kg is not a major obstacle for the hydraulic system, which is designed for much higher loads. For the pneumatic system, there are no significant differences in power at 6 bar (943.3 W) and 8 bar (923.3 W). When moving the piston rod forward and backward, the power did not drop as much as with the hydraulic system, indicating that increasing the load would overload the system.

Today, weight plays an important role in the selection of components. Hydraulic systems are considered robust and compact, but their biggest disadvantage is still the weight of the individual components. The total weight of a hydraulic system (described in the chapter Hydraulic system) is 52,029.3 g (100%), of a pneumatic system (described in the chapter Pneumatic system) 7946.5 g (15.3%) and of an electrical system (described in the chapter Electrical system) 11,636.9 g (22.3%).

Figure  9 shows the comparison of the eight most important parameters between hydraulic, pneumatic and electric systems. The radar chart shows the ratings for each category (velocity, acceleration, force, etc.). The ratings are given from 0 to 10. The displacement rating is directly related to the actuators used (grade 0 = 0 mm, grade 10 = 200 mm), so the hydraulic actuator gets the best rating (9.61) and the electric actuator the worst (4.03). For the evaluation of the velocity (rate 0 = 0 m/s, rate 10 = 0.5 m/s), the average velocity for the execution of the working or backward movement is used. Here the pneumatic actuator received the highest rating (8.38) and the electric actuator the lowest (5.41). Similar to the evaluation of the velocity, the acceleration (rate 0 = 0 m/s 2 , rate 10 = 4 m/s 2 ) was also evaluated according to the average acceleration value for the working or backward movement. Here too, the pneumatic actuator received the highest rate (9.47) and the hydraulic actuator the lowest (4.00). It can be assumed that the reason for the reversal is that the hydraulic system accelerated strongly at first and then could no longer keep up, resulting in a deceleration. The estimated force was divided into tensile and compressive force. For the compressive force, the hydraulic actuator (8.85) achieved the highest rate (rate 0 = 0 N, rate 10 = − 600 N) and the pneumatic actuator (6.01) the lowest. In terms of tensile force (rate 0 = 0 N, rate 10 = 600 N), the hydraulic actuator again achieved the highest rate (8.91) and the electric actuator the lowest (7.66). The opposite is the case when evaluating the power output and power consumption. The lower the rate, the higher the power output or power consumption. The average value was calculated for the power output. The highest rate (rate 0 = 300 W, rate 10 = 0 W) was achieved with the hydraulic actuator (8.56) and the lowest with the electric actuator (2.44). The average value was also determined for the power consumption. The pneumatic system consumed the most energy and was therefore rated 0.92 (rating 0 = 1000 W, rating 10 = 0 W), while the electric system consumed the least and was therefore rated 6.08. The last point of evaluation was weight. Here the hydraulic system received the lowest score (score 0 = 60,000 g, score 10 = 0 g) (1.33) and the pneumatic system the highest (8.68) as the lightest.

Ratings of the individual categories used to compare the three actuation systems.

The diagram in Fig.  9 can be used to select the appropriate actuator or system when certain constraints or requirements are present.

During experimental studies, several advantages and disadvantages of the three systems compared were found: hydraulic, pneumatic and electric.

The highest velocity was achieved with the hydraulic system (0.76 m/s), which then stabilised at 0.18 m/s when the piston rod moved forward, and because of the smaller piston area, the velocity was slightly higher when it moved backwards (0.3 m/s). When the pneumatic piston rod moved forward, the maximum velocity was 0.37 m/s, and the backward velocity of the piston rod was higher (0.64 m/s). As with the hydraulic system, the piston area of the pneumatic cylinder on which the air presses was smaller, so the backward velocity was higher. The electric system was limited to a maximum velocity of 0.3 m/s, which was reached when the piston rod moved forward and backward.

The main obstacle to achieving higher velocities in the hydraulic system is flow, which is limited by power, pump shaft revolutions, system elasticity/pressure accumulation and system parameters. The existing system could be upgraded with a frequency inverter to achieve a higher shaft revolution of the electric motor (and thus the pumps) and thus a higher flow. Flow is also important in a pneumatic system. The compressor compressed as much air as the system needed, so there was no bottleneck as expected (twitching of the pneumatic piston rod). To achieve smooth and constant movement in a hydraulic or pneumatic system (affecting speed, acceleration, etc.), the system must have a feedback loop controller that controls the opening of the valve (hydraulic or pneumatic) and opens or closes it as needed. The installation of hydraulic accumulators helps to prevent sudden releases of high energy potential or pressure shocks.

The advantage of the electric system is that the movement is much smoother, and therefore the velocities and acceleration. The velocities are the same in the forward and backward movement of the piston rod of the electric cylinder because the piston area does not change as in hydraulic and pneumatic systems. In hydraulic and pneumatic systems, this problem could be solved by using double-acting cylinders with double piston rods (same piston area during forward and backward movement). This problem can also be solved by controlling the flow, although in highly dynamic systems it is easier to use double-acting cylinders with double piston rods. However, the disadvantage of the electric system is that to achieve higher velocities, the entire system must be upgraded (larger servo motor, cylinder, controller, higher power, etc.). In hydraulic or pneumatic systems, the velocity can be increased to a certain extent by upgrading the hydraulic power unit or the compressor. To achieve significantly higher velocities, the valves, seals and guide elements in the cylinder must be replaced.

Another major advantage of the electric system is power consumption, as the system only uses as much power as it needs at any given time. Hydraulic and pneumatic systems require more power consumption because of the constant work of the hydraulic power unit and the compressor. The movement and therefore the velocity and acceleration of the electric system are more constant because the servo motor turns the spindle of the electric cylinder with a constant torque in a closed loop, whereas in hydraulic and pneumatic systems there are various pressure losses, pressure shocks, fluctuations in system pressure, etc. The servo motor that turns the spindle or piston rod of the electric cylinder is activated when it receives a signal, while the hydraulic power unit or compressor must work before the valve receives a signal. This can be seen in Fig.  5 f, where the power before the valve is activated is 1529.9 W and drops to about 650 W after the valve has been switched and the piston rod is moving. In Fig.  6 f, there is no significant drop in power before (maximum 943.3 W) and after (minimum 859.2 W) the valve is activated. If the hydraulic power unit or the compressor were to switch on at the moment the valve receives the signal, these two systems would be very slow and would not respond. The hydraulic system could handle even higher loads, depending on the results of the measurements, because the power during the forward and backward movement of the piston rod is much lower than before the valve was activated. The hydraulic system is more robust and can be used in heavy—dirty industries.

A very big disadvantage of hydraulic systems is the large footprint the system requires and the noise it makes. The hydraulic system is heavy and takes up more space because a hydraulic power unit is needed in addition to the actuator, valve, hoses, etc. The pneumatic system weighs only 15.3% of the weight of the hydraulic system, but still requires additional space for the compressor. The electric system weighs 22.4% of the weight of the hydraulic system, but requires minimal floor space as no hydraulic power unit or compressor is needed, only a power socket.

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Acknowledgements

We would like to thank Festo Slovenia for the loan of equipment, especially Dr. Andrej Čebular. This work was supported by the Slovenian Research Agency as part of the research program no. P2-0231.

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Pustavrh, J., Hočevar, M., Podržaj, P. et al. Comparison of hydraulic, pneumatic and electric linear actuation systems. Sci Rep 13 , 20938 (2023). https://doi.org/10.1038/s41598-023-47602-x

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• Original Article
• Open access
• Published: 08 May 2019

Methods to Evaluate and Measure Power of Pneumatic System and Their Applications

• Yan Shi   ORCID: orcid.org/0000-0002-6954-0537 1 , 2 ,
• Maolin Cai 1 ,
• Weiqing Xu 1 &
• Yixuan Wang 1  

Chinese Journal of Mechanical Engineering volume  32 , Article number:  42 ( 2019 ) Cite this article

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Pneumatic system has been widely used throughout industry, and it consumes more than billions kW h of electricity one year all over the world. So as to improve the efficiency of pneumatic system, its power evaluation as well as measurement methods should be proposed, and their applicability should be validated. In this paper, firstly, power evaluation and measurement methods of pneumatic system were introduced for the first time. Secondly, based on the proposed methods, power distributions in pneumatic system was analyzed. Thirdly, through the analysis on pneumatic efficiencies of typical compressors and pneumatic components, the applicability of the proposed methods were validated. It can be concluded that, first of all, the proposed methods to evaluation and measurement the power of pneumatic system were efficient. Furthermore, the pneumatic power efficiencies of pneumatic system in the air production and cleaning procedure are respectively about 35%–75% and 85%–90%. Moreover, the pneumatic power efficiencies of pneumatic system in the transmission and consumption procedures are about 70%–85% and 10%–35%. And the total pneumatic power efficiency of pneumatic system is about 2%–20%, which varies largely with the system configuration. This paper provides a method to analyze and measure the power of pneumatic system, lay a foundation for the optimization and energy-saving design of pneumatic system.

1 Introduction

In pneumatic system, power is transmitted and controlled through compressed air within a circuit. Due to its advantages, such as the low price of its components, easy maintenance of the system, pneumatic system has been widely used throughout industries [ 1 , 2 , 3 ]. And now, pneumatic system has become a main energy consumption system all over the world [ 4 , 5 ].

Energy consumption in Europe of pneumatic system accounts for probably 10% of total industrial power consumption [ 6 ]. The energy consumption of pneumatic system in the United States is about 9% of the whole electricity consumption [ 7 ]. What is more, in China, pneumatic system consumes about 9% of the whole electricity consumption, up to 320 billion kW·h in 2009 [ 8 ].

However, the efficiency of pneumatic system is very limited as a result of improper use, poor management and low efficiency of pneumatic components, about 30% [ 9 , 10 ]. To increase the efficiency of pneumatic systems as well as the implied components, the methods to evaluate and measure the power of compressed air should be established.

Nowadays, the air volume or its rate of flow is applied to represent the air consumption [ 11 ]. But, the amount of power which is lost in supply lines or provided to actuators cannot be clarified, and that is considered extremely important for energy savings. Furthermore, fluid power in hydraulics can be expressed by multiplying the pressure of gauge as well as the volumetric flow rate of the fluid [ 12 , 13 , 14 ]. However, due to the compressibility of air, fluid power is unfeasible for compressed air [ 9 ].

Therefore, to evaluate the energy consumption as well as the loss of pneumatic system, and optimize the design of pneumatic components, it is necessary to propose methods to evaluate and measure power of pneumatic system.

Through more than ten years’ research [ 15 ], we have proposed methods to evaluate and measure the power of pneumatic system. The proposed methods have been employed to analyze the energy loss in different pneumatic system and components [ 16 , 17 , 18 ], and several energy-saving system and components have been developed [ 19 , 20 , 21 ]. What is more, dimensionless optimizations on the pneumatic system are also carried on [ 22 , 23 ].

Wang has studied the optimization method of the pneumatic system in a hybrid power vehicle [ 24 ], he also tried the same optimizing method in the efficiency study of engine [ 25 ], and finally he summarized a frontier technology in the energy-saving technology. In addition, the dimensionless methods have been also applied in the researches of pneumatic system in Wang’s works [ 26 , 27 ], and the principle of efficiency calculating has great reference value.

Moreover, the proposed methods have been proposed to international standard organization as a new work item proposal.

In this paper, firstly, the methods to evaluate and measure the power of pneumatic system were introduced. Secondly, the test installation and method were proposed. Furthermore, an application case of the proposed evaluation and measurement methods was introduced, and power distributions in pneumatic system were illustrated.

This research can be referred in efficiency measurement, analysis and the optimization of pneumatic system and components.

2 Definitions and Symbols

The methods to evaluate and measure the power of pneumatic system, which can be used to analyze pneumatic system energy distribution, and guide the appliers to take the reasonable energy-saving technologies to promote the efficiency of pneumatic system as well as pneumatic components.

2.1 Definitions

In this paper, the terms and definitions given in GB/T 17446 are adopted.

Available Pneumatic Energy E

Potential of compressed air to output mechanical work at the atmospheric state because it goes through a reversible process from a given state to an atmospheric state since all the pneumatic systems and equipment are working at the atmospheric state. Energy conversion in pneumatic system, ideal for compressed air production and consumption are given in authors’ previous work [ 6 ].

Pneumatic Transmission Energy E t

Addresses the energy used to push the air downstream, which is the energy transmitted from the upstream to the downstream. This energy is not included in the internal energy of air.

Pneumatic Expansion Energy E e

Addresses the maximum energy extracted to do mechanical work under atmospheric conditions by means of air expansion. This energy is included in the internal energy of air.

Pneumatic Power P

Pneumatic power P is defined as the flow of aerodynamic available energy that can be extracted from flowing air. The definition, character and application of cases of pneumatic power are given in authors’ previous work [ 8 ].

Pneumatic Transmission Power P t and Pneumatic Expansion Power P e

Pneumatic transmission power P t means the flow of pneumatic transmission energy which is able to be extracted from the flowing air.

Pneumatic expansion power P e is treated as the flow of pneumatic expansion energy which is extracted from the flowing air.

2.2 Symbols and Units

The symbols and units used throughout this paper are shown in Table  1 .

The numerals used as subscripts and the asterisk (*) used as a superscript to the symbols listed in Table  1 are as specified in Table  2 .

3 Assessment and Calculation of Pneumatic Energy

3.1 pneumatic available energy.

The consumed energy with a volume of air consumption and the supplied energy with a volume of air supply should be determined to be pneumatic available energy of the consumed air or the supplied air. As given in authors’ previous work [ 9 ], the available aerodynamic energy consists of two parts: pneumatic transmission energy and pneumatic expansion energy. They can be calculated as follows by Eqs. ( 1 ), ( 2 ) and ( 3 ).

3.2 Pneumatic Power

The consumed power with a consumption of flowing air and the supplied power with a supply of flowing air can be determined to be pneumatic power of consumed air flow or supplied air flow. As given in authors’ previous work [ 9 ], pneumatic power consists of pneumatic transmission power and pneumatic expansion power, and thy is able to be gotten as follows by Eqs. ( 4 ), ( 5 ) and ( 6 ).

When air temperature and the atmosphere temperature are the same, pneumatic power can be given as:

3.3 Evaluation Formula of Atmospheric Temperature

3.3.1 pneumatic available power.

As given in authors’ previous work [ 9 ], pneumatic available power can be obtained by:

3.3.2 Pneumatic Invalid Power

The power which is lost when the air temperature is cooled down till the atmosphere temperature is defined as pneumatic invalid power [ 9 ], which can be given by:

3.3.3 Pneumatic Power Factor

In this standard, the ratio of pneumatic available power and pneumatic power is defined as pneumatic power factor [ 9 ], and it is calculated as shown of the following equations:

3.4 Kinetic Power

Kinetic power of flowing air is one kind of mechanical power, which belongs to available power. Kinetic power can be converted into pneumatic power, and it is calculated as shown of the following equations:

where P k is the kinetic power (W), d means diameter of pipe line (m).

4 Test Installation

Figures  1 , 2 , and 3 illustrate basic circuits that do not include all the safety devices necessary to prevent damage in case of component failure. Because of the safety of personnel and equipment, it is important that those responsible for the testing are responsible.

Test circuit for pipe line test

Circuit which is used for exhaust-to-atmosphere test

Test circuit for exhaust-to-atmosphere test

4.1 Test Circuit for Pipe Line Test

If the pneumatic power of air in pipe line is tested, a suitable test circuit which is shown in Figure  1 should be applied. And Figure  1 (a) shall be used especially when flow, temperature and pressure are measured solely. When integrated instrument, which is composed of flow meter, temperature-measuring instrument and pressure gauge or transduce, is adopted, Figure  1 (b) shall be used. It should be noted that Table  3 is the key to test circuit components.

4.2 Test Circuit for In-Line Test

If the piezometer is connected to both upstream and downstream sides of the element under test, the corresponding test circuit shall be used as shown in Figure  2 . By the way, Table  3 is the key to test circuit components.

4.3 Test Circuit for Exhaust-to-Atmosphere Test

If the component under test, which exhausts directly to atmosphere on its downstream side, a suitable test circuit which is shown in Figure  3 shall be applied. By the way, Table  3 is the key to test circuit components.

4.4 General Requirements

The components to be tested shall be installed and operated in the test circuit in accordance with the manufacturer’s operating instructions. A filter should be installed which provides a standard of filtration specified by the manufacturer of the component under test.

A test circuit shall be constructed from the items listed in Table  3 . Items A to N inclusive are required, and the remaining item O shall be chosen in accordance with Section  4.5 .

All connections for pressure measurement shall be arranged in such a manner that no entrained liquid can be trapped or retained, and a drain may be provided. All connections for pressure measurement shall be arranged in such a manner that no entrained liquid can be trapped or retained, and a drain may be provided.

During the test, pressure drop of flow meter should be no more than 30 kPa.

When temperature of the compressed air is lower than the temperature of the atmosphere by 3 °C, temperature-measuring instrument could not be installed.

4.5 Other Requirements

If the component under test has ports that are different from those described above in structure or size, connectors shall be used for connection with rectifier tubes.

When the inlet and outlet ports of the component under test are different from each other in structure or size, rectifier tubes and pressure-measuring connectors that suited to the relevant ports shall be used.

When the pressure-measuring connector interferes with the body of component under test or an adjacent connector, a special pressure-measuring connector similar to those shown in Figures  4 , 5 , 6 of ISO 6358-2 shall be connected to the component under test using a bent tube or similar mechanism, as shown in Figure  4 . And Table  3 is the key to test circuit components.

Connection of component under test to pressure-measuring connector using a bent tube or similar mechanism

A typical compressed air system

The overall efficiencies of compressors

All special requirements shall be recorded in the test report.

5 Application of the Methods: Power Distributions in Pneumatic System

Because of the proposal of pneumatic power, it is no longer difficult to calculate efficiency of pneumatic equipment and analyze energy distribution in pneumatic system. Besides, classic pneumatic equipment and components, such as compressor, filter, dryer, pipe line, and cylinder, etc. can be measured. This part proposes an application of power distribution in a classic pneumatic system.

The typical pneumatic system which is shown in Figure  5 is divided to 4 parts including production, clean, transport and air consume. Next, efficiencies of typical equipment in these 4 parts are discussed respectively. Meanwhile, the methods to analyze the energy losses and efficiencies of typical pneumatic equipment are introduced.

5.1 In Air Production

Common types of compressors are screw type, piston type, vane type and centrifugal type. Compressors are generally driven by electric motors, which are powered by electricity. The efficiency of the electric motor is generally between 80% and 96%. Smaller motors (< 10 kW) are generally less efficient.

For the power supply of compression mechanism, energy loss will be caused by mechanical friction and mixture, air leakage, insufficient cooling and other reasons. The total of these losses accounted for the lion’s share of all losses. Typically, these losses account for 20%–40% and are different with compressor type and cooling conditions.

Comprehensive efficiency is an important index to evaluate compressor energy conversion. The overall compressor efficiency should be proposed as the following equation:

where the input energy is the total electricity which is supplied to the compressor and the intercooler, and energy output is the output of pneumatic power. The total efficiency as defined above includes the total loss of the compressor. User evaluation of compressor performance is a simple and practical index.

Figure  6 shows the data of some typical compressors in the market. As shown in Figure  6 , the overall efficiency of compressor is within the range of 35% to 50% when the motor power is less than 10 kW, 40%–60% when motor power is 10–100 kW and 51% to 70% when the motor power is higher than 100 kW.

In ISO 1217—Displacement Compressors Acceptance Tests, specific energy which is defined as the ratio of required motor power and air output volume, is prescribed as an energy performance index. The minimum specific energy for compressing air to 0.8 MPa is 0.06 kW·h/m 3 . In practice, however, only large piston compressors approach this number. A well-organized installation is more likely to consume 0.08 to 0.12 kW·h/m 3 .

In Table  4 , it is the picture of actual values for some typical compressors. Considering that the motor power only accounts for 90% of the whole power consumption, then the total efficiency can be calculated. Compared with specific energy, the overall efficiency is necessary condition to evaluate the efficiency of the whole pneumatic system.

5.2 In Air Cleaning

The commonly used compressor types are screw, piston, vane and centrifugal type. Generally, after produced, the compressed air should be sent to the pipe network before the first air dryer and filter purification.

Refrigeration dryer is applied to cool air to the range of 0 to 10 °C. According to Eq. ( 7 ), the aerodynamic power loss through the dryer can also be ignored due to the small internal pressure loss and air leakage. However, power is needed to power the refrigerator and internal cooling fans.

When discussing the efficiency of the entire pneumatic system, this power consumption should be added to the power consumption of the compressor. The following coefficient K dy is recommended:

where Ė 1e is the dryer electricity, Ė 2 is output pneumatic power, η ovr is the whole system efficiency. As is shown in Table  5 , they are actual values of some refrigeration dryers in market.

In the pipe network, the commonly used air purification equipment is a filter. People invented all kinds of filters to remove different kinds of pollutants, such as water, oil, dust and so on. When the compressed air flows through the filter, the power loss is determined by the pressure loss of the filter. In general, the relationship between flow and pressure loss is given in the product catalog. According to this figure, the energy transfer efficiency of the filter is as follows:

where Δ P fl refers the pressure loss. Figure  7 shows an example of a calculation. By computation efficiency of the filter in the market condition of maximum flow rate, concluded that filters the main pipe is always higher than 99%, and a filter performance under 5 μm terminal equipment is about 95%.

Picture of filter pressure loss and energy efficiency

5.3 In Air Transmission

In pipeline transmission, pressure loss and air leakage are two factors that cause aerodynamic power loss.

Pressure losses occur at piping and joints. It can be approximately calculated:

However, in an actual investigation, it is hard to determine coefficient K as well as the average velocity ω because in factories the pipe network is complicated and the air flowing state is disordered. The results are compared with the theoretical results, and the measurement Suggestions are given. Δ P pp is applied as the measured pressure loss, and the power loss is as follows:

When air is not used, a gradual loss of pressure in the system means there is an air leak. Although the allowable amount of air leakage in industrial devices should be 5% or less, in factories, the proportion is as high as 10%–40%.

Leaks occur frequently in pipes, hose fittings, valves, fittings, and terminal equipment. There are several ways to check for leaks. When there are no users on the system, the flow meter in the water supply pipe can be used to check the total leakage of the system. Q lka is given as the flow rate of leaks, and the power being lost is:

The above discussion gives the calculation method of air transmission power loss. Since these power losses vary greatly with system configuration and operating conditions, it seems impossible to display their general data.

5.4 In Air Consumption

It is well known that most of the compressed air is consumed by nozzles and cylinders.

The efficiency and energy distribution of the cylinder are discussed in this paper. For a cycle drive, the energy input can be made up of the following equation:

where V a is air volume consumption of one cycle actuation, and P s is the supply pressure. W wk is given as the mechanical work output, and the efficiency is calculated:

In the meter-out and the meter-in circuit, the velocity controller is used to make the piston speed converge. The speed controller inevitably loses some energy because it restricts the discharge or filling of air in order to keep the air pressure in the cylinder constant. This part of energy can be considered the energy used for velocity control, and represented by W sc in this paper.

Besides W wk and W sc , a mount of supplied energy is exhausted to atmosphere without any application. W nu if given as this part of energy, when there is no internal friction in the cylinder, the piston moves at a constant speed and the air state isothermal changes under the ideal drive of an external circulation of meters, the energy distribution is shown in Figure  8 .

Energy distribution driven by an ideal meter-out cycle

Figure  8 shows that about half of the power supply is effectively used for mechanical work and piston speed control. Since the energy consumed by non-use is expansion energy, the reuse of expansion energy is the key to energy saving of gas cylinders.

For the actual drive, the energy assigned to internal friction, piston acceleration and heat transfer is very small compared with the above three parts. Figure  8 can approximately describe the energy distribution in the actual drive.

5.5 Power Flow in Pneumatic Systems

Based on the above analysis, the efficiencies of the four parts can be summed up as shown in Table  6 . The total efficiency of a pneumatic system is less than 20%.

Take a simple practical pneumatic system as an example, pneumatic power is used to analyze efficiency of some components and energy flow in the system, which is shown in Figure  9 . Table  7 shows the related details of power flow minutely for reference.

Energy flow in a pneumatic system

6 Conclusions

In this paper, methods to evaluate and measure the power of pneumatic system were introduced. With the methods, power distributions in pneumatic system were analyzed. Through this paper, it can be concluded that:

The methods to evaluate and measure power of pneumatic system are proposed, which lays a foundation for development of the energy-saving pneumatic technologies and components.

Consider that motor power accounts for only 90% of the total power consumption, the overall pneumatic power efficiencies of piston compressor, vane compressor, screw compressor and centrifugal compressor are about 39.6%–73.1%, 42.1%–52.7%, 45.4%–57.2% and 59.9%–63.4%.

The pneumatic power efficiency of pneumatic system in the cleaning procedure is 85%–90% approximately, and the pneumatic power efficiencies of after-cooler, dryer and filter are about 99%, 90%–96% and 95%, respectively.

The pneumatic power efficiencies of pneumatic system in the transmission and consumption procedures are about 70%–85% respectively.

The total pneumatic power efficiency of pneumatic system is about 2%–20%, which varies largely with the system configuration.

Therefore, the ways to improve the efficiency of pneumatic system are: isothermal compression of compressor, partial pressure supply of pipeline network, reduction of pressure loss and compressed air leakage, and so on.

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Authors’ Contributions

YS was in charge of the whole trial; YS also wrote the whole manuscript; WX assisted with sampling and laboratory analyses. All authors read and approved the final manuscript.

Authors’ Information

Yan Shi is a professor at School of Automation Science and Electrical Engineering , Beihang University , China . In addition, he obtained his doctoral degree in mechanical engineering from Beihang University , China . He is major in intelligent medical devices, pneumatic control system and energy-saving of pneumatic systems.

Maolin Cai is a professor at School of Automation Science and Electrical Engineering , Beihang University , China . He got his doctor degree from Tokyo Institute of Technology , Japan . He is specialized in intelligent medical devices, technology of high efficiency and large scale compressed air energy storage.

Weiqing Xu is a lecturer at School of Automation Science and Electrical Engineering , Beihang University , China . He got his doctor degree in mechanical engineering from Beihang University , China . His research interests include intelligent mechanical devices and high efficient compressed air energy storage technologies.

Yixuan Wang received his B.E degree from School of Automation Science and Electrical Engineering , Beihang University , in 2015. He is currently a Ph.D. candidate at Beihang University , China . His research interests include fuel and power systems of UAV, fluid control, measurement and control system.

Competing Interests

The authors declare that they have no competing interests.

Supported by National Natural Science Foundation of China (Grants Nos. 51675020, 51375028) and Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems.

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Shi, Y., Cai, M., Xu, W. et al. Methods to Evaluate and Measure Power of Pneumatic System and Their Applications. Chin. J. Mech. Eng. 32 , 42 (2019). https://doi.org/10.1186/s10033-019-0354-6

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Received : 02 July 2017

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DOI : https://doi.org/10.1186/s10033-019-0354-6

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• Pneumatic system
• Compressed air
• Energy assessment
• Measurement
• Power evaluation

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Zhiwen Wang 1 , Bo Yang 1 , Qian Ma 2 , Hu Wang 1 , Rupp Carriveau 3 , David S-K. Ting 3 and Wei Xiong 1, *

1 Department of Mechanical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China 2 Department of Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China 3 Turbulence and Energy Laboratory, Ed Lumley Centre for Engineering Innovation, University of Windsor, Windsor N9B 3P4, Ontario, Canada E-mail: [email protected]; [email protected] *Corresponding Author

Received 24 July 2022; Accepted 30 March 2023; Publication 06 November 2023

Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

Keywords: Pneumatic system, pneumatic cylinder, energy monitoring, fault diagnosis, exergy, machine learning.

1 Introduction

The manufacturing sector consumes about half of the global total energy generation and about 90% of industrial energy consumption [ 1 , 2 ]. Increasing pressures from government regulations, energy prices, market forces, and sustainability awareness, etc. are pushing manufacturing enterprises to accept Intelligent Manufacturing and Green Manufacturing and to upgrade their production lines and management systems. Energy Management Systems (EMS) and Prognostic and Health Management (PHM) are critical enablers for Green Manufacturing and Intelligent Manufacturing. Generally, EMS and PHM are separately considered and operated in manufacturing workshops. In recent years, the novel interactive EMS-PHM Paradigm (i.e., Energy-Based Maintenance Paradigm) has drawn great attention and evolved rapidly [ 3 – 6 ]. Manufacturing energy data needs to play a more important role in decision-making processes.

Essentially, for a complex multi-energy domain coupled electromechanical system, the production processes are the controls of energy/power flows, that is, the energy/power flow can affect the states of components and systems. That means the energy data could be used for energy efficiency optimization, condition monitoring, fault diagnostics and prognostics, and final quality management. Figure 1 shows the Fault-Energy-Quality nexus framework which illustrates the interactions between fault states, energy consumption, and quality of products in automatic production systems. Abnormal energy consumption modes imply faults or degradations of components/systems, subsequently low-quality products. Conversely, bad quality issues are generally caused by faults or degradations of components/systems, and faults or degradations lead to abnormal energy consumption modes. In this framework, the energy data is easily obtained and monitored.

Figure 1 Fault-Energy-Quality nexus framework.

In automatic production systems, energy is generally transmitted and converted by mechanical, electrical, hydraulic, and pneumatic approaches. Hereinto, pneumatic systems are widely used in various automatic workshops, such as automobile assembly, automatic welding, electronics assembly, canning and packing, and robotics. This confirms the well-established, highly valued attributes of pneumatic systems, such as low investment costs, simple structure, easy maintenance, environmental friendliness, and long service life, etc. [ 7 – 9 ]. However, compared with mechanical, electrical, and hydraulic systems, the pneumatic system presents two obvious disadvantages. First of all, energy efficiency with a value of less than 15% is significantly lower than that of other transmission systems [ 10 , 11 ]. Even though the initial capital investment of a pneumatic system is much lower than hydraulic and electric systems, the high energy cost caused by low efficiency can generally account for 70% ∼ 80% of the total life cycle cost. Air is clean and cheap, but compressed air is one of the most expensive energy carriers. To date, there is no systematic and uniform standard for evaluating the energy performance of pneumatic systems [ 12 , 13 ]. Secondly, the development of intelligent fault diagnoses and prognoses technologies for pneumatic components/systems are trailing behind others. This could be attributed to a couple of things: (i) the investigation cost of pneumatic components/systems are very low, and the reliabilities are relatively high, therefore complicated and expensive sensors are not judged an economically prudent option for investment, and; (ii) the particularly non-linear and coupling properties of compressed air increase the fault diagnoses and prognoses challenges for pneumatic components/systems. Thus, to maintain a competitive edge for pneumatics in this time of Intelligent Green Manufacturing, these shortcomings must be overcome. A more dedicated and economic fault diagnosis and prognostic approach is needed. Here we submit that the integration of energy management and fault diagnostics and prognostics of pneumatic components and systems could be a game changer in this space. The first critical issue is to solidly prove the scientific hypothesis that there is a mapping relationship between energy modes and fault modes in pneumatic components/systems.

Thus, in this study, we take the most representative actuator pneumatic cylinder as an example, and we prove that the exergy data can be used for fault diagnosis with the help of machine learning methods. Further, we prove that the exergy signal always outperforms the single pressure signal and flowrate signal as a diagnostic, irrespective of the operating conditions. This evidence is critical for further developing an Exergy-Based Maintenance Paradigm for complex pneumatic systems. The organization of this paper is as follows: In Section 2, an overview of the state of the art of fault diagnosis, energy and exergy analysis in pneumatic systems is provided and analysed. Section 3 provides the methodology used in this study, including exergy calculation, experiments, data processing, and machine learning methods. Results are analysed and discussed from different perspectives in Section 4. Finally, conclusions are drawn in Section 5.

2 Literature Review of Fault Detection and Diagnosis in Pneumatic Systems

In these times of Industry 4.0, increasingly more pneumatic enterprises are developing intelligent management and fault diagnosis technologies by taking advantage of the Internet of Things (IoT), big data, and machine learning, etc. In recent years, several industrial products and services have been released. For instance, in 2021, EMERSON-AVENTICS launched the plug-and-play Smart Pneumatics Analyzer and Smart Pneumatics Monitor for preventative maintenance and energy optimization by continuously monitoring the flow of air in a system to detect leaks in real-time [ 14 ]. Almost simultaneously, by taking advantage of machine learning, FESTO-RESOLTO launched the Festo Automation Experience (Festo AX) software platform with functions of preventive maintenance, monitoring the quality in production and energy consumption [ 15 ]. However, more time is needed to fairly assess the true efficacy of these ‘black-box’ products. Some would say there is still a notable gap between promising scientific research developments and industrial applications. That is, compared to other power/energy transmission systems, intelligent fault diagnosis in pneumatic systems is in its infancy from the perspective of scientific research, let alone industrial applications. For example, leakage is one of the most common faults and contributes to 10% ∼ 40% of energy waste in pneumatic systems. The existing leak detection and positioning of pneumatic systems/components in most workshops are still primitive, complex, and limited: suds and hearing. Some workshops introduce a more reliable option, the ultrasonic leak detector; nevertheless, it is not in real-time, it is costly both in terms of labour and time, and it is incapable of detecting internal leakages. In recent years, some pneumatic functional modules have been developed to detect downstream leakages by periodically detecting pressure decrease and flowrate increase [ 16 , 17 ]. However, the locations of leakage points cannot be provided by these pneumatic functional modules.

In general, fault diagnosis in pneumatic systems can be classified into three categories: experience-based, model-based, and data-driven approaches.

Experience-based fault diagnosis is currently the most widely used approach in manufacturing workshops. It is quite simple and economical to implement. However, its function is highly dependent on the experience of workers. Unfortunately, few workers are competent to carry out a reliable and accurate diagnosis of pneumatic systems and components. The maintenance manuals provided by manufacturers generally only show some common and static faults under limited conditions [ 18 , 19 ]. Although the experienced-based approach can be used when historical data is very limited, it is generally less reliable than the other two approaches, especially when the system and environment change dynamically [ 4 ]. The widely recommended Fault Tree Analysis (FTA) can be regarded as a systematic and normalized experienced-based method [ 20 ]. However, solid and thorough knowledge of pneumatic systems/components is the precondition for accurate diagnosis. Generally, this is feasible for the designer but not the user. Besides, the experienced-based approach is generally used for breakdown maintenance. In other words, the fault diagnosis is not conducted until the facilities/plants break down. For a more convenient automation application, experience-based approaches can be further extended and embedded in computer-aided expert systems based on sound, knowledge, and experience [ 21 – 24 ].

Model-based fault diagnosis is largely founded on mathematic models of systems and components, which are generally deduced from phenomenological laws and apriori knowledge about systems and components. Theoretically, once the mathematic model is established, a model-based approach should provide higher accuracy than the other two approaches. However, the cost is generally very high and a model can be limited to a specific system [ 4 ]. The determinations of some critical parameters are usually bottlenecks. For a complex system, it is very difficult and sometimes impractical to establish an accurate mathematic model, especially when considering interaction, nonlinearity, randomness, and degradation. The accuracy of fault diagnosis drops dramatically with a coarse model. The situation is more challenging for pneumatic systems due to the strong coupling of pressure, flowrate, and temperature of compressed air. Therefore, there are almost no established model-based fault diagnosis cases in terms of complex pneumatic systems except several cases on pneumatic components and small test benches [ 25 – 31 ].

Data-driven fault diagnosis can be regarded as the extension of conventional signal-based approaches with the booming IoT, big data, and machine learning, etc. It is a cutting-edge topic in the field of FDD (Fault Detection and Diagnosis) and has been widely and successfully applied in a variety industry sectors [ 32 , 33 ]. No solid white-box mathematic models are needed, and representative features can be extracted from the historical and new data. Accordingly, it could also be the most promising enabler for FDD in pneumatic systems. In the past decades, many data-driven methods with various data signals have been investigated. Such data includes pressure of compressed air, flowrate of compressed air, acoustic emission, electric energy consumption, vibration of components, temperature, position, displacement/velocity/acceleration, etc. Leu et al. [ 34 ] conducted some fault diagnosis simulations of pneumatic circuits based on a bidirectional LSTM (Long Short-Term Memory) Neural Network. Five types of signals, including internal and external pressure, temperature, flowrate of compressed air, and piston displacement of the pneumatic cylinder, were used as data set inputs for training the LSTM model. The results showed that the accuracy of the bidirectional LSTM model is higher than that of a typical LSTM model and traditional diagnosis methods and the accuracy of specific fault diagnosis was higher than 95%. It should be noted that the data sets were collected via simulations and not experiments, as some signals were not practically available workshops. Li and Kao [ 35 ] introduced a method for diagnosing external leakage faults on both sides of the pneumatic cylinder using wavelet methods and pattern recognition. The multiresolution wavelet decomposition of various sensor signals such as pressure, flowrate, etc. was employed to extract features for final classification. The method worked but the accuracy was not released. The process of feature extraction was relatively complex and the universality was unknown. Demetgul et al. [ 36 ] evaluated the fault diagnosis performance of a combination of eight sensors and ANNs (Artificial Neural Networks) on a didactic modular production system (MPS) from the Festo Company. These sensors include pressure sensor of main system, magazine optics sensor, vacuum information P/E switch, material handling P/E switch, linear potentiometer, material handling arm pressure sensor, vacuum analogy pressure sensor, and material in the stock optic sensor. The 25 features of signals were calculated for identifying 11 faults. The results showed that ANNs could be used for the diagnostic of even extremely repetitive automation systems and more sensors were needed for accurately diagnosing more faults. Therefore, they recommended that the number of the fault modes to be kept below 5 for a more reliable diagnosis. Zachrison and Sethson [ 37 ] explored the feasibility of detecting the operating changes of a rod-less pneumatic cylinder by using self-organizing maps. Various combinations of displacement, velocity, and pressure signals were used for eigenvector. It was found that more features and more sensors did not mean a better detection result for a specific change. Santolamazza et al. [ 38 , 39 ] thought that it was an interesting method to detect and predict faults by analysing signals of energy consumption because the degradation and energy performance were always intertwined. Therefore, they explored the possibility of real-time detecting anomalous behaviours of a compressed air generation system in a manufacturing factory. A baseline (healthy state) model of energy consumption was created by using artificial neural networks (ANNs). And then the deviation from the baseline was calculated by comparing the actual energy consumption with the baseline, which could imply the anomalous behaviours. Besides, they further evaluated the availabilities of other machine learning techniques for better energy consumption control in compressed air generation systems. Similarly, Benedetti et al. [ 40 ] and Bonfá et al. [ 41 ] also proved the feasibility of controlling energy performance and detecting anomalous behaviours of compressed air generation systems by analysing energy consumption data. Abela et al. [ 42 , 43 ] investigated the feasibility of detecting downstream leakages of compressed air in pneumatic systems by calculating the pressure changes in the upstream. The experimental results showed that it is feasible in a limited and controlled simple test system but not so in more complex systems. Ertel et al. [ 44 ] thought that it was too expensive and impractical to monitor the individual pneumatic components and every pneumatic system was typically unique, therefore it was more practical to identify faulty operation from normal operation at the system level instead of detailed fault diagnosis of individual pneumatic components. Thus, with only flowrate data, they successfully identified the faulty state of a simple pneumatic test system using one-class-learning algorithm nearest neighbour-data-description. Kovacs and Ko [ 45 ] identified the fault states of pneumatic cylinders based on real data from a factory using various unsupervised machine learning methods. Qi et al. [ 46 ] proposed a fault-diagnosis system for reciprocating compressors used in petroleum industry by taking advantage of big data and machine learning. Big data of five years, including vibration, pressure, temperature, noise, and position signals, were collected for feature extraction. The results showed that the system could identify most of potential faults with more than 80% accuracy. Mahmoud et al. [ 47 ] and Czopek et al. [ 48 ] explored the feasibility of detecting leakages of pneumatic components by analysing acoustic emission signals caused by leakages of compressed air. Pneumatic valves are also the essential components in pneumatic systems. There are several studies on fault detection and diagnosis of pneumatic valves with different data-driven and machine learning methods, such as principal component analysis (PCA), Bayesian classification, adaptive neuro-fuzzy inference, and ANNs etc. [ 49 – 54 ].

Overall, fault detection and diagnosis of pneumatic systems and components are in high demand, but this knowhow is in its infancy. Based on our surveys of previous studies, the following defects still need to be overcome.

• It is very difficult to conduct FDD when considering the faults on both system level and individual component level. Current studies focus on either system or component.

• Generally, more, and more diverse sensors are needed for diagnosis of different faults.

• Fault detection is relatively easier but fault diagnosis is difficult.

• Some theoretical studies are notionally good, but have not been tested in an actual workshop environment.

• Data-driven approaches are the most promising. Data-driven modelling and hybrid modelling could make up for the shortfalls of traditional model-based approaches. However, in terms of pneumatic systems, data-driven FDD is still maturing, that is, the self-learning and self-adaptive functions are still not available.

According to the introduction and literature review, we propose that combining exergy data with machine learning could help to overcome these shortcomings to some extent, advancing FDD in pneumatic systems. This study is the first step to test this hypothesis.

3 Methodology

The use of the data-driven methods for FDD usually involves the following steps: data selection, data preprocessing, feature extraction, classifier building, and fault detection and diagnosis. Thus, in this section, we first explain why the exergy data is selected to estimate the energy performance of pneumatic systems. Then, a pneumatic experimental setup is designed for collecting data and simulating internal and external leakage faults. Collected exergy data is preprocessed and then sent to a stacked Auto-encoder (SAE) for feature extraction. Subsequently, a support vector machine (SVM) classifier is trained for FDD. Finally, several detailed experiments are designed to validate the methodology.

3.1 Exergy of Compressed Air

Generally, for evaluating the energy performance of pneumatic systems at the system level and the component level, three approach categories can be summarized, including the calculation of the mass or volume of compressed air, the thermodynamic first-law-based approach, and the thermodynamic second-law-based approach. Different approaches are utilized in different systems and applications by different vendors and users. Nevertheless, in recent years, the second-law-based exergy analysis has been widely investigated and proven to be a more solid criterion than others for evaluating energy performance of pneumatic systems [11, 55–58]. Actually, exergy-based methods have been widely used in many fields, and a standard analytical framework has been developed [ 59 ]. Most recently, thanks to the powerful capacity of presenting detailed and reliable energy information, exergy-based methods have also been used for energy performance prediction and predictive control with the help of machine learning [ 60 – 63 ]. Besides, from the perspective of data fusion, exergy of compressed air can be regarded as a data fusion criterion of pressure, flowrate, and temperature. More information can be implied from exergy than other single data. Thus, in this study, the exergy is used for representing the energy data for FDD.

In pneumatic systems, there are no chemical reactions and the changes of kinetic energy and potential energy of compressed air are generally negligible. Thus, the chemical exergy, kinetic exergy, and potential exergy of compressed air are not considered. The total exergy can be represented by the physical exergy as expressed as

where E ˙ x is the total exergy flowrate in J/s, m ˙ is the mass flowrate of compressed air in kg/s, e x is the specific exergy in J/kg, h is the specific enthalpy in J/kg, s is the specific entropy in J/kg/K, T is the temperature in K, and subscript 0 signifies the reference state.

In industrial pneumatic systems, the pressure of compressed air is generally below 1 MPa, therefore, the compressed air can be regarded as an ideal gas. The specific exergy can be calculated by

where c p is the constant-pressure specific heat of compressed air in J/kg/K, R g is the gas constant in J/kg/K, and p is the pressure in Pa.

Exergy represents the maximum amount of useful work that can be obtained as the system is brought to equilibrium with the environment. Thus, a clear definition of the reference environment is essential. In this study, the pressure and temperature of atmospheric reference state are set as 101325 Pa and 25 ∘ C, respectively.

The temperature fluctuations of environment and system are negligible in the pneumatic system investigated in this study. Thus, the exergy of compressed air is further simplified and calculated by the following equation.

3.2 Experimental Setup

For leakage faults detection and diagnosis of the pneumatic cylinder, an experimental setup of a typical pneumatic system is built as shown in Figure 2 . Compressed air is generated via a variable-speed air compressor providing a near-constant pressure compressed air storage tank. A pressure regulating valve is set downstream of the storage tank to regulate the pressure in the downstream system. The pneumatic cylinder is controlled by a three-position five-way solenoid directional control valve. A single full operation cycle of the pneumatic cylinder includes an expansion stroke and a contraction stroke. The external leakage faults are simulated by controlling a flow control valve in the rod port side. The internal leakage faults are simulated by damaging the sealing ring artificially. Pressure sensor 1 and flow sensor 1 are installed upstream of the pneumatic cylinder. The flow sensor is a type of FESTO SFAB. The accuracy of flowrate metering is ± (3% o.m.v. + 0.3% F.S.). The flow measuring range is 6 ∼ 600 L/min for flow sensor 1. The pressure sensor is a type of SMC ISE40A. The accuracy of pressure metering is ± 2% F.S. The pressure measuring range is - 0.1 ∼ 1.000 MPa. The acquired pressure data and flowrate data are used for calculating exergy data. The combined uncertainty of exergy is about ± 4% of the expected value of exergy in the main range of measurement of this study. Flow sensor 2 is used for measuring and controlling the flowrate of external leakage. It is also a FESTO SFAB flow sensor. The flow measuring range is 0.5 ∼ 50 L/min. All sensor and pneumatic components can be enabled and regulated via the PC for data acquisition and control. ADVANTECH PCI-1710U data acquisition card is used for DAQ and the software system is programmed via LabVIEW. The frequency of data acquisition is set at 100 Hz.

Figure 2 Experimental setup of a typical pneumatic system.

Figure 3 Acquired data from sensors.

Figure 4 Flowchart of data preprocessing.

3.3 Data Preprocessing

Generally, there is some interference information in the raw data caused by errors of sensors and experimental setup. With data preprocessing, the redundant information in raw data can be eliminated, thereby facilitating the following feature extraction and learning processes. Figures 3 (a), 3 (c), and 3 (e) show the intercepted raw data of pressure, flowrate, and exergy, respectively. Figure 4 shows the flowchart of data preprocessing. In this study, the data preprocessing is conducted in Python. First, the missing data and anomalous data are detected and handled. Normalization is then performed for mapping data into the section [ 0 , 1 ] without changing the distribution of raw data. Figures 3 (b), 3 (d), and 3 (f) show the intercepted normalized data of pressure, flowrate, and exergy, respectively. Due to the periodicity of raw data, the cycle segmentation is conducted, and the raw data flow is split into individual cycles corresponding to a single operation cycle of a pneumatic cylinder. Each cycle of the pneumatic cylinder is regarded as a sample used for feature extraction and machine learning. The frequency of data acquisition is set as 100 Hz and the period of one cycle of the cylinder is set as 2 seconds. Thus, one sample contains 200 data points as shown in Figures 3 (b), 3 (d), and 3 (f). The start of the cycle is identified through a peak-detection method in Python. Finally, the samples are stored in a table. In this study, training data and test data are preprocessed using the same methods. Besides, it is evident that the profile of exergy is more similar to that of flowrate from Figure 3 . This means the exergy is more flowrate dependent. According to Equation ( 3 ), there is a logarithm relation between exergy and pressure, and there is a positive proportional relationship between exergy and flowrate. Thus, the change of exergy is relatively small when the pressure changes.

Figure 5 Process of machine learning.

3.4 Machine Learning Methods

Figure 5 shows the flowchart of the training and testing classifier model with machine learning. The dimensions of a single preprocessed sample are 200. It is necessary to carry out a dimensionality reduction for feature extraction and selection. After testing and comparing the artificially defined features (mean value, kurtosis, peak value, and root-mean-square, etc.) with features extracted from PCA and AE (autoencoder), AE is here finally adopted as the method of feature extraction. There are many variants of AE, including SAE, VAE (variational autoencoder), and RAE (regular autoencoder), etc. According to the testing results, SAE is accepted in this study. Figure 6 shows the detailed structure of SAE. SAE is composed of several cascaded AEs with many hidden layers. Encoder and decoder are neural networks. In the encoder, the high dimensional data can be significantly reduced to the latent features with very low dimensions. The decoder can restore the low-dimensional features to the original high-dimensional data, which could be used for verifying the performance of SAE.

Figure 6 Structure of SAE.

The extracted and selected features from SAE are sent to the machine learning model to train the classifier. In this study, SVM is the dominating method used for supervised learning of the classifier by taking advantage of its outstanding performance in handling small sample data sets. Then the neural network method is used for comparison. The performance of the classifier model is continually evaluated and optimized by adjusting the parameters of SAE and SVM. Finally, the testing data sets are utilized to validate the accuracy of the classifier.

3.5 Experiments

Table 1 shows the normal and faulty conditions used in experiment #1, including two normal operating conditions and four external/internal leakage faults. Due to the periodicity and regularity of operations of the experimental pneumatic system, a small sample of data is acquired. In this study, the total training data set contain 480 (80 × 6) samples, and each sample is composed of 200 points. The experiment is repeated twice for acquiring data of training set and test set respectively. It is worth noting that the training set and test set are randomly selected from the original data sets. Training and test processes are repeated five times, and the final result is the average of five individual results. Finally, the training and test data sets of each operating condition consisted of 34 samples and 14 samples, respectively. Table 2 shows the numbers of the training and test set and the corresponding labels of the six different operating conditions.

Table 1 Normal and faulty conditions used in experiments

Table 2 Training and testing data sets and labels of different operating conditions

When the pressure change of the pneumatic system is not significant, it is predicted that detecting the change of operating condition is more difficult. It is necessary to investigate which kind of data among pressure, flowrate, and exergy is capable of detecting this small difference. Thus, another set, supplementary experiment #2, is conducted as shown in Table 3 . The difference of pressure between two operating conditions is only 0.02 MPa.

Table 3 Detailed information of experiment #2

After experiments, the acquired data is preprocessed and then sent to SAE for feature extraction and selection. Relu and Sigmoid are selected as the activation functions in SAE, and the SAE network structure is set as [200, 100, 50, 10, 2]. The number of hidden layers is set as 4 for the encoder, and the number of iterations is 300. The features after dimensionality reduction are sent to SVM model for learning. The kernel function of the SVM is RBF (Radial Basis Function), and the penalty factor and parameter of kernel function are 10 and 0.1, respectively. Five tests are conducted, and each accuracy of fault diagnosis is recorded.

4 Results and Discussions

In this section, the availabilities of SAE and SVM are verified with a loss curve and K-Fold Cross Validation. After that, the results of experiment #1 and experiment #2 are presented and discussed respectively. The diagnostic accuracy of various data (pressure, flowrate, and exergy) is compared and analysed. Then the results of different machine learning models (SAE + SoftMax neural network model and SAE + SVM model) are compared.

4.1 Availability Verification of SAE and SVM

Because SAE belongs to the neural network and the size of the training dataset is very small, the overfitting problem is likely to appear. Thus, the validation dataset is split from the training data. And the loss curves of the training dataset and validation dataset are analysed for evaluating the overfitting of SAE model. The overfitting problems can be solved by optimizing hyperparameters (e.g., the number or the size of the layers). Figure 7 shows the loss curves of the training dataset and validation dataset of pressure, flowrate, and exergy, respectively. It is clear that the training loss and validation loss converge to zero, therefore, overfitting is not present. It also means the SAE model can effectively reduce dimensions and reconstruct the original data. Figures 8 (a), (b), and (c) shows the original and reconstructed data of pressure, flowrate, and exergy, respectively. Each figure includes data of all six operating conditions in one test. As highlighted by circles, some distinguishing features can be more easily identified with reconstructed data of SAE.

Figure 7 Loss curve of SAE.

Figure 8 Original and reconstructed data of SAE.

The aim of machine learning is to achieve generalized models that could perform well on never-before-seen data. Underfit will be evident if the established model is very simple, while overfit will appear if the model is too complex. In this study, the size of the training dataset is very small. As shown in Figure 9 , the K-Fold Cross Validation method with hyperparameter c = 10 is adopted to measure the general capacity of established models. Figure 10 shows the learning curves of different data with K-Fold Cross Validation. It is evident that the model performs well with validation sets and there is no overfitting.

Figure 9 K-Fold Cross Validation (K = 10).

Figure 10 Learning curves of different data with K-fold cross validation0

4.2 Comparison of Pressure, Flowrate, and Exergy Results

4.2.1 Results and discussion of experiment #1

Table 4 shows the testing accuracy of various signals with SAE and SVM. The average accuracy of pressure, flowrate, and exergy are 83.6%, 97.4%, and 93.6%, respectively. The accuracy of test 2 and test 4 of the pressure signal are only 60% and 71% respectively. Overall, the established fault diagnosis models with flowrate and exergy data perform much better than that with pressure data.

Figure 11 shows the confusion matrices of various signals in test 2. In terms of pressure data, it is clear that normal #2 condition with label 2 and internal leakage #2 condition with label 3 cannot be accurately identified. Samples with label 2 and label 3 are rather indistinct, thereby contributing to an extremely low accuracy of 60%. On the other hand, samples of flowrate and exergy data can be accurately identified except for slightly ambiguous label 0 and label 5.

The results presented in Table 4 and Figure 11 are highly dependent on the feature extraction capacity of SAE model. In this study, the selected two-dimensional features of pressure, flowrate, and exergy data are visualized with the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm as shown in Figure 12 . It is evident that SAE model performs better when extracting features of flowrate and exergy data than that of pressure data, especially in terms of label 2 and label 3.

Table 4 Testing accuracy of pressure, flowrate, and exergy signals in experiment #1

Figure 11 Confusion matrixes of various signals in test 2.

Figure 12 Visualization of selected features with DBSCAN clustering.

Exergy can be regarded as the fusion of pressure and flowrate. The diagnostic accuracy with exergy data is still very high even though the diagnostic accuracy with pressure data is extremely low. It implies that exergy is more significantly affected by flowrate rather than pressure. To validate this implication, the grey correlation analysis is conducted. Grey correlation analysis can measure the correlation among factors according to the similarity or difference of development trends of factors. The confusion between label 2 and label 3 directly contributes to the bad performance of pressure data. Thus, the data with label 2 and label 3 is selected for grey correlation analysis. In this study, exergy is regarded as the reference, and pressure and flowrate are used for comparisons. The results are shown in Table 5 . It is clear that the correlation between flowrate and exergy is larger than the correlation between pressure and exergy. Exergy is more affected by flowrate than pressure in this experiment. Thus, the diagnostic accuracy with exergy data is almost as high as that with flowrate data. Nevertheless, the negative impact of pressure on exergy still cannot be neglected, which results in a relatively lower exergetic accuracy over flowrate.

Table 5 Grey correlation analysis of pressure, flowrate, and exergy

4.2.2 Results and discussion of experiment #2

Similar to those in Section 4.1, the loss curves and learning curves of pressure, flowrate, and exergy data are shown in Figures 13 and 14 , respectively. The model performs well on validation sets and there is no overfitting. Table 6 shows the testing accuracy of various signals with SAE and SVM. The average accuracy of pressure, flowrate, and exergy are 100%, 93.4%, and 100%, respectively. The result of experiment #2 is opposite to that of experiment #1, and the accuracy of pressure is higher than that of flowrate. Table 7 shows the results of the grey correlation analysis. Exergy is still more affected by flowrate than pressure. However, in experiment #2, pressure imposes a positive impact on exergy, thereby contributing to a higher accuracy of exergy than flowrate.

Figure 13 Loss curves of SAE in experiment #2.

Figure 14 Learning curves of different data with K-Fold Cross Validation in experiment #2.

Table 6 Testing accuracy of pressure, flowrate, and exergy signals in experiment #2

Table 7 Grey correlation analysis of pressure, flowrate, and exergy in experiment #2

According to the results of experiment #1 and #2, we could conclude that the diagnostic accuracy of pressure data and flowrate data are highly dependent on operating conditions, while the diagnostic accuracy of exergy data remains high irrespective of the operating conditions. The exergy is more affected by flowrate than pressure because the pressure change is not large in this study. The pressure could impose both positive and negative impacts on exergy under different operating conditions.

Although the accuracy of pressure and exergy are higher than that of flowrate as shown in Table 6 . But it does not mean the flowrate loses its effectiveness in the Experiment #2. The average accuracy of flowrate is 93.4% which is still an acceptable result. This only means the pressure and exergy perform better than flowrate under these operating conditions.

4.3 Comparison of Different Machine Learning Methods

In order to explore if the results change with different machine learning methods, a SAE + SoftMax neural network model is also studied to compare with SAE + SVM model. Figure 15 shows the structure of the SAE + SoftMax neural network model. SAE is used for dimensionality reduction and SoftMax is for classification. The weights of the encoder are frozen when training SAE. After training, the last two layers are unfrozen for fine tuning. The SAE + SoftMax neural network model also runs 5 times for getting a final average accuracy.

Figure 15 Structure of SAE + SoftMax neural network model.

Table 8 Testing accuracy of various signals with SAE + SoftMax in experiment #1

Table 9 Testing accuracy of various signals with SAE + SoftMax in experiment #2

Tables 8 and 9 show the testing accuracy of various signals with SAE + SoftMax model in experiment #1 and experiment #2 respectively. Overall, the accuracy of SAE + SoftMax is lower than that of SAE + SVM. This is mainly because the number of samples in the training set is too small for a neural network model while SVM is good at learning with small sample data sets. Nevertheless, it is clear that the trends are consistent with the results of the SAE + SVM model. For experiment #1, the diagnostic accuracy of exergy data is lower than that of flowrate data but higher than that of pressure data. For experiment #2, the diagnostic accuracy of the exergy data is lower than that of pressure data but higher than that of flowrate data.

There is no doubt that the accuracy of diagnosis could be improved with a more sophisticated machine learning method. Whatever methods are used, the accuracy of diagnosis is mainly dependent on if the ‘changes’ caused by the faults could be effectively identified by the machine learning algorithm. With the same machine learning method, which data is better? It is decided by the change of which data is more evident and can be easier identified. In the Experiment #1, the change of flowrate is more evident than that of pressure when comparing the normal and fault conditions under the same operating pressure. Therefore, the accuracy of flowrate is higher than that of pressure. While, in the Experiment #2, the change of pressure is more evident than that of flowrate although the pressure difference is only 0.02 MPa. Therefore, the accuracy of pressure is higher than that of flowrate. It should also be noted that the result of accuracy is an averaged value. It varies in individual tests, especially in Experiment #1 when the operating conditions are more complex than those in Experiment #2. The accuracy of pressure is higher than that of flowrate in test (1) in Table 4 and test (2) in Table 8 , although the average accuracy of pressure is much lower than that of flowrate.

5 Conclusions

Intelligent Manufacturing and Green Manufacturing are pushing manufacturing enterprises to upgrade their FDD and energy management systems. Pneumatic components and systems are widely used in manufacturing sectors. However, compared with other power transmission technologies, FDD and energy management of pneumatic technology are trailing behind due to various factors. In this study, based on a Fault-Energy-Quality nexus framework, we have integrated energy management and fault diagnostics/prognostics of pneumatic components and systems. This study is the first attempt to prove the fundamental hypothesis that there is a mapping relationship between energy modes and fault modes in pneumatic components/systems. Exergy is adopted to evaluate the energy performance of compressed air. Machine learning methods are used for establishing FDD models. A typical pneumatic circuit with a pneumatic cylinder was taken here as an example for the experiment. The main conclusions from this study are:

• Exergy could be used for evaluating energy performance and also for representing the fault conditions of pneumatic systems and components.

• Pressure, flowrate, and exergy data are all effective for fault detection and diagnosis in pneumatic systems. However, the indicator performance of these data is highly dependent on operating conditions and systems/components. Nevertheless, the exergy data, as a fusion of pressure and flowrate, is always effective with high accuracy.

• Data-based machine learning is a powerful enabler of FDD for pneumatic systems and components, although different accuracy is obtained with different methods. The advantage of exergy data is its consistency in performing well with different machine learning methods.

• With upstream sensor data, internal and external leakages of a downstream pneumatic cylinder can be successfully detected and diagnosed. This implies that it is possible to detect and diagnose more faults with fewer sensors in pneumatic systems with the help of exergy and machine learning.

This study attempts to develop a new exergy-based maintenance approach for complex pneumatic systems. Even though the experiments and models are not very complex, the preliminary results substantiate that it is feasible to develop an exergy-based maintenance program in simple pneumatic systems. What about complex systems with more components and more faults? This issue will be investigated in our next study.

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant [number 51905066 and number 62002039].

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Biographies

Zhiwen Wang received the bachelor’s degree in Marine Engineering from Dalian Maritime University in 2012 and the philosophy of doctorate degree in Marine Engineering from Dalian Maritime University in 2018, respectively. He is currently working as an Associate Professor at the Department of Mechanical Engineering, Dalian Maritime University. His research areas include energy saving and FDD of pneumatics, thermodynamics, and energy storage.

Bo Yang received the bachelor’s degree in Mechanical Engineering from Zaozhuang University in 2020. He is now pursing the master’s degree in Mechanical Engineering in Dalian Maritime University. His research areas include energy saving and FDD of pneumatics, and machine learning.

Qian Ma received her B.D., M.D. and Ph.D. degrees in computer software and theory from Northeastern University in 2011, 2013, and 2019, respectively. She is currently an associate professor with the College of Information Science and Technology, Dalian Maritime University, Dalian, China. Her research interests include missing data imputation, data management and artificial intelligence.

Hu Wang received the bachelor’s degree in Mechanical Engineering from Ludong University in 2019 and the master’s degree in Mechanical Engineering from Dalian Maritime University in 2022, respectively. He is currently pursuing the doctorate degree in Dalian Maritime University. His research areas include energy storage, and fluid power transmission and control.

Rupp Carriveau is a Professor of Civil and Environmental Engineering, University of Windsor, Canada. He is the director of Turbulence & Energy Lab. He received his BSc in Civil Structural Engineering from University of Windsor. He obtained his MSc and PhD in Fluids Engineering from Western University, Canada. His research interests cover energy storage, renewable energy, and systems optimization etc.

David S-K. Ting is a Professor of Mechanical, Automotive & Materials Engineering, University of Windsor, Canada. He received his BSc in 1989 from University of Manitoba. He obtained his MSc and PhD in 1992 and 1995 from University of Alberta, Canada. His major research interests are turbulence, heat transfer, energy & thermal systems, renewable energy, and aerodynamics.

Wei Xiong is a Professor of Mechanical Engineering, Dalian Maritime University. He is the director of Ship Electromechanical Equipment Institute. He received his PhD in the Faculty of Mechatronic Engineering from Harbin Institute of Technology, China. His major research interests are fluid power and control, marine rescue, and compressed air energy storage.

International Journal of Fluid Power, Vol. 24_4 , 643–682. doi: 10.13052/ijfp1439-9776.2442 © 2023 River Publishers

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Optimised design and simulation analysis of a double-row pneumatic injection seeding device.

1. Introduction

2. materials and methods, 2.1. principles and design of the dual-row pneumatic injection seeding device, 2.2. mechanical analysis of the broadcasting process, 2.3. pneumatic injection seeding bench test, 2.4. simulation test, 3. results and discussion, 3.1. pneumatic injection seeding bench test procedure analysis, 3.2. simulation and analysis of soil penetration process, 3.3. simulation of aerodynamic shot seeding analysis, 3.3.1. outlet pipe diameter, 3.3.2. mixed zone length, 3.3.3. mixing zone inlet diameter, 3.4. orthogonal test results, 3.5. verification of seeding performance, 4. conclusions.

• By analysing the mechanism of pneumatic seed insertion, and analysing the process of seed insertion, acceleration, insertion, and contact with the soil, the high-speed camera has determined that the conditions for seed insertion can be met at 200,000 Pa and a vertical speed of 26.59 m/s.
• When the inlet air pressure is 100,000 Pa, the vertical velocity of the seed reaches the requirement of shot seeding through the optimisation of the structure. It was found that the best results were achieved when the diameter of the outlet pipe D C was 2 mm, the length of the mixing zone H was 10 mm, and the diameter of the inlet of the mixing zone D was 15 mm. In order to verify the optimised structural parameters, seed casting performance tests were carried out in this study. The experimental results showed that under this device, the vertical velocity of stem mustard seed grain was about 32.3 m/s, which could be completely and stably shot seeded into the soil. The above results showed that the optimised pneumatic stem mustard injection seeding device was able to achieve pneumatic shot seeding of seeds.
• Based on the current technology for precision sowing of small-sized seeds, this paper applies pneumatic shot seeding to stem mustard sowing, which satisfies both precision sowing of small-sized seeds and sowing under no-tillage conditions, and enables two rows to be sown at the same time. The future research will focus on precision sowing and improve and optimise the seed feeding effect. This will be followed up by observing the germination rate of the seeds, as well as examining whether different shot seeding pressures cause damage to the seeds.

Author Contributions

Institutional review board statement, data availability statement, conflicts of interest.

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Niu, Q.; Yu, W.; Yan, W.; Zhang, H.; Wang, L.; Li, C.; Wang, C. Optimised Design and Simulation Analysis of a Double-Row Pneumatic Injection Seeding Device. Agriculture 2024 , 14 , 1376. https://doi.org/10.3390/agriculture14081376

Niu Q, Yu W, Yan W, Zhang H, Wang L, Li C, Wang C. Optimised Design and Simulation Analysis of a Double-Row Pneumatic Injection Seeding Device. Agriculture . 2024; 14(8):1376. https://doi.org/10.3390/agriculture14081376

Niu, Qi, Wei Yu, Wenhao Yan, Helong Zhang, Lihong Wang, Chengsong Li, and Chunlei Wang. 2024. "Optimised Design and Simulation Analysis of a Double-Row Pneumatic Injection Seeding Device" Agriculture 14, no. 8: 1376. https://doi.org/10.3390/agriculture14081376

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Correction: Systemic review of age brackets in pediatric emergency medicine literature and the development of a universal age classification for pediatric emergency patients - the Munich Age Classification System (MACS)

• Alexander Althammer 1 , 2 ,
• Stephan Prückner 1 ,
• Geogr Christian Gehring 1 ,
• Victoria Lieftüchter 3 ,
• Heiko Trentzsch 1 &
• Florian Hoffmann 3  

BMC Emergency Medicine volume  24 , Article number:  145 ( 2024 ) Cite this article

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The Original Article was published on 25 July 2023

BMC Emergency Medicine (2023) 23:77

https://doi.org/10.1186/s12873-023-00851-5

The original article contains errors in the proposed age classification relating to missing days between Neonates and Infants, unclear distinction between age brackets, and unclear end of adult age bracket.

An updated version of the mMACS can be viewed ahead in this Correction article.

Furthermore, the amendments noted above were suggested by Daniel Pfeiffer of the Dr. v. Hauner Children’s Hospital, Munich, Germany who is acknowledged via this Correction article.

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Alexander Althammer, Stephan Prückner, Geogr Christian Gehring & Heiko Trentzsch

Department of Anesthesiology, Universitätsklinikum Augsburg, Stenglinstraße 2, 86156, Augsburg, Germany

Alexander Althammer

Pediatric Intensive Care and Emergency Medicine, Dr. von Hauner Children’s Hospital, Ludwig- Maximilians-University, Lindwurmstraße 4, 80337, Munich, Germany

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Althammer, A., Prückner, S., Gehring, G.C. et al. Correction: Systemic review of age brackets in pediatric emergency medicine literature and the development of a universal age classification for pediatric emergency patients - the Munich Age Classification System (MACS). BMC Emerg Med 24 , 145 (2024). https://doi.org/10.1186/s12873-024-01064-0

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Introducing reconfigurable manufacturing systems to agriculture

• Published: 16 August 2024

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• Yongbing Feng 1 ,
• Guohua Gao 1 ,
• Pengyu Wang 1 ,
• Zihua Zhang 1 &
• Kai Liu 1  

Reconfigurable manufacturing systems (RMS), which possess the advantages of both dedicated manufacturing lines (DML) and flexible manufacturing systems (FMS), are increasingly accepted concept in industries, however, little has been done to discuss that RMS provide new opportunities and value in the agriculture community. Furthermore, as the agriculture transitions from mass production to mass customization or individualization, RMS will become a critical system architecture that satisfies the production requirement. This paper follows the guidelines of systematic literature review (SLR) and adopts a mixed-method to investigate the expected benefits of RMS for agriculture. We review limited literature within RMS in agriculture, covering years 2006–2023, from which we identify 29 use cases and compare them with 84 use cases in other application areas between 2018 and 2022. We compare 5 reported benefits, 6 reconfigurable characteristics and 3 technology readiness levels to assess the extent of RMS application in agriculture. We distill the RMS characteristics that can provide benefits to agriculture from the reviewed RMS applications. Then, inspired by RMS applications in agriculture and other application areas, we provide a roadmap for RMS in agriculture, consisting of different specific levels of RMS. Lastly, we conclude this paper by identifying the distinctive characteristics of agricultural RMS (ARMS) in terms of the inherent variability of agricultural products, research topics, and enabling technologies, and by giving a clear definition to encourage researchers and practitioners to develop studies in this field.

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This work was supported by the National Key R&D Program of China (Grant No. 2023YFD2001100), Beijing Science and Technology Planning Project (Grant No. Z221100006422007), R&D Program of Beijing Municipal Education Commission (Grant No. KM202310005034), and the National Natural Science Foundation of China (Grant No. 52305003).

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This researcher wants to replace your brain, little by little

The US government just hired a researcher who thinks we can beat aging with fresh cloned bodies and brain updates.

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A US agency pursuing moonshot health breakthroughs has hired a researcher advocating an extremely radical plan for defeating death.

His idea? Replace your body parts. All of them. Even your brain. 

Jean Hébert, a new hire with the US Advanced Projects Agency for Health ( ARPA-H ), is expected to lead a major new initiative around “functional brain tissue replacement,” the idea of adding youthful tissue to people’s brains. 

President Joe Biden created ARPA-H in 2022, as an agency within the Department of Health and Human Services, to pursue what he called  “bold, urgent innovation” with transformative potential. 

The brain renewal concept could have applications such as treating stroke victims, who lose areas of brain function. But Hébert, a biologist at the Albert Einstein school of medicine, has most often proposed total brain replacement, along with replacing other parts of our anatomy, as the only plausible means of avoiding death from old age.

As he described in his 2020 book, Replacing Aging, Hébert thinks that to live indefinitely people must find a way to substitute all their body parts with young ones, much like a high-mileage car is kept going with new struts and spark plugs.

The idea has a halo of plausibility since there are already liver transplants and titanium hips, artificial corneas and substitute heart valves. The trickiest part is your brain. That ages, too, shrinking dramatically in old age. But you don’t want to swap it out for another—because it is you.

And that’s where Hébert's research comes in. He’s been exploring ways to “progressively” replace a brain by adding bits of youthful tissue made in a lab. The process would have to be done slowly enough, in steps, that your brain could adapt, relocating memories and your self-identity.  

During a visit this spring to his lab at Albert Einstein, Hébert showed MIT Technology Review how he has been carrying out initial experiments with mice, removing small sections of their brains and injecting slurries of embryonic cells. It’s a step toward proving whether such youthful tissue can survive and take over important functions.

To be sure, the strategy is not widely accepted, even among researchers in the aging field. “On the surface it sounds completely insane, but I was surprised how good a case he could make for it,” says Matthew Scholz, CEO of aging research company Oisín Biotechnologies, who met with Hébert this year. 

Scholz is still skeptical though. “A new brain is not going to be a popular item,” he says. “The surgical element of it is going to be very severe, no matter how you slice it.”

Now, though, Hébert's ideas appear to have gotten a huge endorsement from the US government. Hébert told MIT Technology Review that he had proposed a $110 million project to ARPA-H to prove his ideas in monkeys and other animals, and that the government “didn’t blink” at the figure. 

ARPA-H confirmed this week that it had hired Hébert as a program manager. 

The agency, modeled on DARPA, the Department of Defense organization that developed stealth fighters, gives managers unprecedented leeway in awarding contracts to develop novel technologies. Among its first programs are efforts to develop at-home cancer tests and cure blindness with eye transplants .

President Biden created ARPA-H in 2022 to pursue “bold, urgent innovation” with transformative potential.

It may be several months before details of the new project are announced, and it’s possible that ARPA-H will establish more conventional goals like treating stroke victims and Alzheimer’s patients, whose brains are damaged, rather than the more radical idea of extreme life extension. 

“ If it can work, forget aging; it would be useful for all kinds of neurodegenerative disease,” says Justin Rebo, a longevity scientist and entrepreneur.

But defeating death is Hébert's stated aim. “I was a weird kid and when I found out that we all fall apart and die, I was like, ‘Why is everybody okay with this?’ And that has pretty much guided everything I do,” he says. “I just prefer life over this slow degradation into nonexistence that biology has planned for all of us.”

Hébert, now 58, also recalls when he began thinking that the human form might not be set in stone. It was upon seeing the 1973 movie Westworld , in which the gun-slinging villain, played by Yul Brynner, turns out to be an android. “That really stuck with me,” Hébert said.

Lately, Hébert has become something of a star figure among immortalists, a fringe community devoted to never dying. That’s because he’s an established scientist who is willing to propose extreme steps to avoid death. “A lot of people want radical life extension without a radical approach. People want to take a pill, and that’s not going to happen,” says Kai Micah Mills, who runs a company, Cryopets, developing ways to deep-freeze cats and dogs for future reanimation.

The reason pharmaceuticals won’t ever stop aging, Hébert says, is that time affects all of our organs and cells and even degrades substances such as elastin, one of the molecular glues that holds our bodies together. So even if, say, gene therapy could rejuvenate the DNA inside cells, a concept some companies are exploring , Hébert believes we’re still doomed as the scaffolding around them comes undone.

One organization promoting Hébert's ideas is the Longevity Biotech Fellowship (LBF), a self-described group of “hardcore” life extension enthusiasts, which this year published a technical roadmap for defeating aging altogether. In it, they used data from Hébert's ARPA-H proposal to argue in favor of extending life with gradual brain replacement for elderly subjects, as well as transplant of their heads onto the bodies of “non-sentient” human clones, raised to lack a functioning brain of their own, a procedure they referred to as “body transplant.”

Such a startling feat would involve several technologies that don’t yet exist, including a means to attach a transplanted head to a spinal cord. Even so, the group rates “replacement” as the most likely way to conquer death, claiming it would take only 10 years and $3.6 billion to demonstrate.

“It doesn’t require you to understand aging,” says Mark Hamalainen, co-founder of the research and education group. “That is why Jean’s work is interesting.”

Hébert's connections to such far-out concepts (he serves as a mentor in LBF’s training sessions) could make him an edgy choice for ARPA-H, a young agency whose budget is $1.5 billion a year.

For instance, Hebert recently said on a podcast with Hamalainen that human fetuses might be used as a potential source of life-extending parts for elderly people. That would be ethical to do, Hébert said during the program, if the fetus is young enough that there “are no neurons, no sentience, and no person.” And according to a meeting agenda viewed by MIT Technology Review , Hébert was also a featured speaker at an online pitch session held last year on full “body replacement,” which included biohackers and an expert in primate cloning.

Hébert declined to describe the session, which he said was not recorded “out of respect for those who preferred discretion.” But he’s in favor of growing non-sentient human bodies. “I am in conversation with all these groups because, you know, not only is my brain slowly deteriorating, but so is the rest of my body,” says Hébert. “I'm going to need other body parts as well.”

The focus of Hébert's own scientific work is the neocortex, the outer part of the brain that looks like a pile of extra-thick noodles and which houses most of our senses, reasoning, and memory. The neocortex is “arguably the most important part of who we are as individuals,” says Hébert, as well as “maybe the most complex structure in the world.”

There are two reasons he believes the neocortex could be replaced, albeit only slowly. The first is evidence from rare cases of benign brain tumors, like a man described in the medical literature who developed a growth the size of an orange. Yet because it grew very slowly, the man’s brain was able to adjust, shifting memories elsewhere, and his behavior and speech never seemed to change—even when the tumor was removed. 

That’s proof, Hébert thinks, that replacing the neocortex little by little could be achieved “without losing the information encoded in it” such as a person’s self-identity.

The second source of hope, he says, is experiments showing that fetal-stage cells can survive, and even function, when transplanted into the brains of adults. For instance, medical tests underway are showing that young neurons can integrate into the brains of people who have epilepsy  and stop their seizures.  

“It was these two things together—the plastic nature of brains and the ability to add new tissue—that, to me, were like, ‘Ah, now there has got to be a way,’” says Hébert.

“I just prefer life over this slow degradation into nonexistence that biology has planned for all of us.”

One challenge ahead is how to manufacture the replacement brain bits, or what Hebert has called “facsimiles” of neocortical tissue. During a visit to his lab at Albert Einstein, Hébert described plans to manually assemble chunks of youthful brain tissue using stem cells. These parts, he says, would not be fully developed, but instead be similar to what’s found in a still-developing fetal brain. That way, upon transplant, they’d be able to finish maturing, integrate into your brain, and be “ready to absorb and learn your information.”

To design the youthful bits of neocortex, Hébert has been studying brains of aborted human fetuses 5 to 8 weeks of age. He’s been measuring what cells are present, and in what numbers and locations, to try to guide the manufacture of similar structures in the lab.

“What we're engineering is a fetal-like neocortical tissue that has all the cell types and structure needed to develop into normal tissue on its own,” says Hébert. 

Part of the work has been carried out by a startup company, BE Therapeutics (it stands for Brain Engineering), located in a suite on Einstein’s campus and which is funded by Apollo Health Ventures, VitaDAO, and with contributions from a New York State development fund . The company had only two employees when MIT Technology Review visited this spring, and the its future is uncertain, says Hébert, now that he’s joining ARPA-H and closing his lab at Einstein.

Because it’s often challenging to manufacture even a single cell type from stem cells, making a facsimile of the neocortex involving a dozen cell types isn’t an easy project . In fact, it’s just one of several scientific problems standing between you and a younger brain, some of which might never have practical solutions. “There is a saying in engineering. You are allowed one miracle, but if you need more than one, find another plan,” says Scholz.

Maybe the crucial unknown is whether young bits of neocortex will ever correctly function inside an elderly person’s brain, for example by establishing connections or storing and sending electro-chemical information. Despite evidence the brain can incorporate individual transplanted cells, that’s never been robustly proven for larger bits of tissue, says Rusty Gage, a biologist at the Salk Institute in La Jolla, Calif., and who is considered a pioneer of neural transplants. He says researchers for years have tried to transplant larger parts of fetal animal brains into adult animals, but with inconclusive results. “If it worked, we’d all be doing more of it,” he says.

The problem, says Gage, isn’t whether the tissue can survive, but whether it can participate in the workings of an existing brain. “I am not dissing his hypothesis. But that’s all it is,” says Gage. “Yes, fetal or embryonic tissue can mature in the adult brain. But whether it replaces the function of the dysfunctional area is an experiment he needs to do, if he wants to convince the world he has actually replaced an aged section with a new section.”

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