Hydraulic powered systems in mobile machines suffer from low operating efficiency during multiple actuator operation owed to large throttling losses when metering the flow to low pressure actuators. State of the art throttling based systems suffer from these losses, demand higher engine power to supply for peaks, couple the prime mover to the dynamic nature of the load and offer no energy recuperation or regeneration. Significant improvement in energy efficiency of mobile machines can be achieved by addressing these drawbacks. This paper presents a novel multi-pressure system aiming to reduce throttling losses, handles power peaks locally, regenerates and stores energy and decouples the supply system from load dynamics. In the multi-pressure system, Proportional bidirectional poppet type 2/2 valves are used to select the best possible pressure line from four levels maintained by integrated hydraulic accumulators while simultaneously adjusting the flow for the actuators, piston and rod sides. A controller is studied and presented for the novel system and the challenges are also discussed. Additionally, possibilities to improve the energy efficiency is also illustrated. Simulation results from a 20-ton wheeled excavator work functions show good controllability and tracking performance as well as regeneration of energy during lowering of the boom. Reduction in energy consumption compared to a traditional load sensing system is also realizable although difficulties defining the novel system's saving abilities is present.
Volumetric flow rate sensors are used in various technical applications. Therefore, it is interesting to use volumetric flow sensors that neither obstruct nor manipulate the flow to be measured nor are restricted to certain flow types and profiles. For this reason, the virtual volumetric flow sensor was developed. A test rig was constructed to validate this soft sensor, which can generate laminar, turbulent, steady, and unsteady flow rates. The dynamic part of the flow is generated by coupling three cylinders and operating a servo valve. In this work, an experimental hydraulic test platform capable of generating reproducible high-frequency oscillatory flow rates is investigated as an enabling system for validating a pressure-based virtual volumetric flow sensor. Two gain-scheduled PID control strategies are implemented to realize the required excitation profiles. Both controllers were intensively investigated on the test rig for various high-frequency scenarios, including pulsations up to 80 Hz. At 80 Hz, the direct controller achieves a normalized mean absolute error of 36.3% (including phase delay). After phase alignment, waveform fidelity corresponds to an nMAE of 10.1%, demonstrating suitability for high-frequency soft-sensor validation. The comparative results show that direct velocity control remains effective up to excitation frequencies of 80 Hz, while indirect position-based control becomes ineffective above approximately 40 Hz due to inherent phase delay. Eventually, the generated dynamic flow rate is utilized to demonstrate the high accuracy of the soft sensor for an oscillation of 20 Hz.
The importance of machine learning (ML) in various engineering disciplines has steadily increased over the past several years, primarily due to the rise in computational power and the development of new, powerful algorithms. ML methods have also found significant applications in fluid power technology, offering substantial benefits such as enhanced system performance, optimized design processes, and improved predictive maintenance. These methods are increasingly used to handle complex, nonlinear systems in fluid power, providing advanced solutions for simulation, system design, control strategies, and condition monitoring. In particular, ML techniques can process vast amounts of data to predict system behavior, identify faults, and optimize energy efficiency, leading to more reliable and efficient fluid power systems. This review aims to introduce and discuss the wide range of ML applications and ongoing research in fluid power, offering engineers a comprehensive overview of the available literature. By doing so, we aim to help engineers identify and select the most suitable and promising ML methods to address their specific tasks and challenges.
Knowledge of the level of entrained air in pump inlets is important for hydraulic system design. Entrained air levels, often quantified as gas void fractions, can change with operation of hydraulic systems, so monitoring entrained air during operations can provide substantial design insight to a hydraulic system. The performance of a SONAR-based entrained air measurement system was investigated on a test hydraulic system utilizing a Coriolis meter as a reference. Two instances of the measurement system were installed on the inlet line of a hydraulic pump in series with a Coriolis meter. One instance of the SONAR-based entrained air measurement system was installed to measure the gas void fraction (GVF) within the flow tubes of a Coriolis flow meter, and a second instance of the SONAR-based entrained air measurement system was installed to measure the GVF within in a section of hydraulic hose in series with the Coriolis meter. Data were recorded to evaluate the SONAR-based entrained air measurement systems both upstream and downstream, as well as collocated with, the Coriolis meter. Speed of sound (SOS) measurements were acquired within the aerated hydraulic oil in the inlet line as the GVF was varied utilizing a variable area flow restriction installed within the pump inlet line. Increasing the restriction reduced the pressure and increased the GVF within the hydraulic fluid due to (1) the existing gas expanding and (2) additional out-gassing from the hydraulic oil. The measured SOS was utilized to determine a gas volume fraction (GVF) within each instance of the SONAR-based GVF measurement system. Additionally, GVF was also calculated from the density measurements of a Coriolis flow meter. GVF measurements from the SOS measurement test section across the Coriolis meter were highly correlated with GVF calculated from Coriolis meter density measurements. The SOS GVFs were shifted up by up to 0.126 % GVF and matched with <0.2% RMSE. For the upstream or downstream SOS GVF measurements, there was variation from those calculated from Coriolis meter density measurements. These differences were anticipated due to pressure differences in the pipe section from that in the Coriolis meter. These pressure differences resulted in differences in the air released from the hydraulic fluid and differing GVFs in the two sections. These results show that the SOS measurement technique can accurately measure the entrained air status of hydraulic systems.
Low-cost fault diagnosis of pneumatic systems has been highly demanded by the industrial community in recent years. In this study, the feasibility of low-cost and low-redundancy fault diagnosis in complex pneumatic systems is investigated by using a minimal number of mature flow and pressure sensors. A pick-and-place demonstration system with 17 pneumatic actuators is taken as the experimental platform. Only one pressure sensor and one flow sensor are utilized to diagnose 132 leakage faults with the help of a one-dimensional convolutional neural network (1D CNN). The average accuracies of leakage fault diagnosis with pressure, flow rate, and exergy data are 89.5%, 96.8%, and 98.9%, respectively. The results are interpreted with Class Activation Mapping (CAM) and Occlusion Sensitivity Analysis (OSA). Overall, it reveals that it is feasible to diagnose multiple faults in complex pneumatic systems with a minimal number of commonly used sensors.
In the context of electrification and the development of quieter internal combustion engines, the noise generated by axial piston units is becoming more prominent. While established methods and standards exist for evaluating airborne and fluid-borne noise of hydraulic pumps, the structure-borne noise emitted by the pump and transferred to mobile machinery has rarely been investigated. A method that has gained popularity in recent applications for characterizing structure-borne noise sources is the in situ blocked force method. Blocked forces are receiver-system-independent interface quantities, allowing for a component-specific assessment of structure-borne noise. This publication introduces the fundamental approaches for determining blocked forces and presents the results of a metrological assessment of blocked forces for an axial piston pump. The findings demonstrate promising out-comes according to common validation methods for blocked forces and provide two straightforward single-valued metrics for evaluating the emitted structure-borne noise of an axial piston pump.
In recent years, the electrification of mobile machinery has advanced. One crucial issue is that the energy density of the associated storage solutions is lower, requiring more space on the machines or more frequent recharging. To compensate for this the hydraulic system must be more efficient and sustainable. In this context, the use of decentralized electro-hydrostatic actuators (EHA) shows high potential. However, they are not yet widely used. The reasons for this are likely higher initial component costs, worse dampening, and the high cost of implementing necessary control software. This paper presents a simulation-based evaluation of an electro-hydrostatic actuator with a hydraulic high-speed unit intended for use in mobile machinery. The circuit layout was selected based on cost considerations and incorporates load-holding capability. Furthermore, the load-holding valves are a key aspect of the control scheme. They are used to introduce a small amount of active throttling on the outflow side of the cylinder while energy is supplied to it. This extends the load pressure range in which the hydraulic unit is pumping and enables full operational coverage of all working conditions, which would otherwise be impossible. The developed controller synchronizes the valves' switching times with motor speed to enhance system performance and prevent excitation. The controller is linked with a lumped parameter model of the hydraulic circuit and validated based on test cases that focus specifically on the quadrant switches, as well as on a measured dig and dump cycle. The results qualitatively demonstrate the excellent performance of the EHA, with mostly brief and minor velocity deviations during quadrant switching. The combination of the developed EHA and corresponding controller achieves good position tracking during the load cycle once drift due to inherent control delays is compensated for by a simple P-controller, representing the machine operator adjusting to the system response. This reduces the maximum RMSE of position deviation from 163 mm to 42 mm. In conclusion, the results demonstrate the functionality of the developed EHA and encourage further investigation, particularly experimental validation, to confirm the findings.
Data-driven fault detection is crucial for hydrostatic drives and axial piston units (APUs) due to their central role in both conventional and electrified powertrains. The reliability of their internal roller bearings is pivotal to prevent machine downtime and guarantee high efficiency. However, detecting bearing faults in End-of-Line (EoL) serial testing for quality control is particularly challenging. Strong hydraulically induced noise and, most notably, significant manufacturing-related serial dispersion across units often mask the subtle fault signatures. To address this, this paper introduces a novel experimental methodology designed to simulate a realistic EoL scenario. By systematically interchanging drive shafts and housings among a set of seven units to create 30 unique component combinations, the inherent serial dispersion found in production is effectively replicated. This innovative approach allows for the efficient testing of distinct manipulted bearings against a realistic backdrop of component variability. Using vibroacoustic data from this setup, the present study integrates semi-supervised machine learning (ML) with a comparative analysis of different sensors and signal processing techniques. It is demonstrated that a fixed accelerometer on the test bench, when combined with a knowledge-based bandpass filter for envelope spectrum analysis, provides the most robust fault detection. This optimized configuration consistently achieves an Area Under the Curve (AUC) exceeding 0.95, effectively separating faulty from healthy units despite the challenging conditions. The findings provide a clear framework for implementing a reliable and automated fault detection system in industrial manufacturing, proving that data-driven quality control can succeed even in high-variance, noisy production environments.
This study presents a coupled Elastohydrodynamic Lubrication (EHL) simulation model for a multi-lobe radial piston motor and its experimental validation under high-load and low-speed conditions. These operating regimes pose challenges such as severe wear and excessive power loss due to complex lubricating interfaces, which are difficult to characterize experimentally. To address this, the model solves a density-based Reynolds equation incorporating multi-body dynamics, throttling losses, and elastic deformations of components. Simulation results reveal that lubrication regimes and asperity contact pressures depend strongly on chamber pressure and motor speed. The roller-bushing interface operates under mixed lubrication, while the piston-cylinder interface exhibits boundary lubrication during high-load conditions due to severe asperity contact. Piston tilt and asymmetric deformation significantly affect film thickness and pressure distribution of lubricating interfaces. Incorporating a friction model based on experimental data enabled a realistic analysis of power loss, identifying the upper piston-cylinder interface and throttling loss as major contributors. The model provides a detailed frame-work for simulating and analyzing tribological behaviors in radial piston motors and can be used to evaluate the effects of design parameters such as clearance, geometry, and material properties.
Piston-type positive displacement machines are used across diverse applications and operating conditions, posing a critical design challenge to minimize solid-body contact while maintaining high efficiency. This study investigates the potential of hydrostatic pockets between the cylinder block and valve plate to provide dynamically and passively controlled pressure forces, mitigating contact issues at low speeds without excessive losses at high speeds. Simulations of a baseline pump design revealed persistent solid-body contact under low-speed and high-pressure conditions, indicating the need for enhanced lubrication strategies. Retaining the baseline design, the study examined multiple hydrostatic pocket configurations through simulation, varying their location, quantity, and size. Furthermore, this study also investigates the size of the grooves, which act as constant-area orifices connecting the hydrostatic pockets and displacement chambers. Although the primary focus is on low-speed high-pressure and high-speed high-pressure scenarios, additional operating points at low-speed low-pressure, high-speed low-pressure, and medium-speed medium-pressure are also considered. The effectiveness of each design is evaluated on the basis of film thickness, contact pressure, leakage, torque, and viscous losses under key operating conditions. The simulation results are then compared with the experimental findings reported in prior literature, and they suggest that placing the hydrostatic pockets farther from the displacement chambers leads to greater improvements, particularly in reducing leakage, minimizing viscous losses, and avoiding metal-to-metal contact. This paper seeks to deliver a better understanding of the hydrostatic pockets and the corresponding groove orifices, offering design guidance for optimizing the lubrication management for future piston-type positive displacement machines and informing strategies for improved efficiency and longevity in demanding applications.
Multi-fault diagnosis of the axial piston pump plays a vital role in ensuring the safety and reliability of modern hydraulic transmission and control systems. Current intelligent fault diagnosis methods demonstrate effective performance but fail to generalize if new fault patterns occur. Simply fine-tuning these models only with newly collected data leads to the catastrophic forgetting problem, whereas retraining a new fault diagnosis model with the entire historical data is both resource-intensive and time-consuming. Therefore, a novel class-incremental learning method based on dual-aligned knowledge distillation is proposed for multi-fault diagnosis of the axial piston pump, which can continually learn new fault patterns and preserve fault diagnosis ability on old fault patterns with a limited amount of historical data. On the one hand, the consistency between output-logits of the previous model and that of the current one is enforced in the incremental learning process to mitigate catastrophic forgetting. On the other hand, intermediate feature relationships with different important weights are aligned to further retain fault diagnosis performance on old fault patterns. Both the comparison experiment and the ablation experiment demonstrate the effectiveness of the proposed method.
Electro-hydrostatic actuators are widely used in aerospace, industrial, and off-road machinery due to their efficiency and self-contained design. However, gear-pump degradation increases internal leakage, reducing performance and elevating failure risk. This paper proposes a health-aware control framework that accounts for pump degradation in closed-loop control. Internal leakage progression is modeled with a nonlinear Wiener process and the degradation state is estimated online with an extended Kalman filter. The degradation state is used within a nonlinear model predictive controller to adjust control actions using predicted remaining useful life. The approach is evaluated in nonlinear simulations and compared with a proportional-integral-derivative controller. Results indicate that the proposed controller mitigates leakage-induced performance loss and increases predicted pump remaining useful life by 2.75% while maintaining acceptable position tracking accuracy. Although the improvement is modest under accelerated degradation, the framework is suited to slowly evolving degradation and supports predictive maintenance of safety-critical actuation systems.
Motivated by energy efficiency and decreasing the amount and size of components, recent studies have presented hydraulically actuated systems that include one or several fluid short circuit connections between actuator chambers. The main motivations for establishing short circuit connection have been to enable hydraulic power sharing directly between hydraulic actuators in terms of cylinders and motors, thereby reducing conversion losses and enabling reduced power installations in hydraulic drive networks. This paper expands the general theory of hydraulic short circuit connections by generically analyzing the consequences of short circuit connections. This analysis is used to define which short circuiting schemes are physically feasible and which inhibit the full functionality of a machine. Furthermore, a generic method is presented on how to identify every feasible short circuiting scheme for any number of double acting hydraulic actuators.
The hydraulic pitch system is a critical component of modern wind turbines, responsible for both power regulation and safety mechanisms. Ensuring the reliability and availability of this system is essential for optimal turbine performance. This study focuses on the reliability of less active yet essential components within the hydraulic pitch system, including the pump, accumulator, relief valve, hose, and hydraulic oil. The hydraulic oil in the analysis has been treated as a component also. Employing a comprehensive Fault Tree Analysis (FTA), this study identifies the failure modes, effects, and root causes of these components. Key findings indicate that contamination and inadequate maintenance are primary contributors to failures. The study discusses various fault identification and condition monitoring algorithms, including those based on artificial intelligence and machine learning, which are effective in post-processing data for fault detection. Physics-based models, such as observer methods like the Kalman Filter, show potential for real-time implementation. The findings underscore the importance of stringent filtration, regular inspections, and proactive maintenance strategies, including monitoring accumulator pre-charge pressure and using appropriate hydraulic oil. Addressing these root causes can significantly enhance the reliability and longevity of hydraulic pitch systems, thereby improving the overall performance and safety of wind turbines.
Over a decade after the introduction of the Industry 4.0 vision, digital transformation remains a central imperative for industrial companies aiming to maintain competitiveness amid increasing economic and ecological pressures. As various Industry 4.0-implementations enter industrial practice, a more precise understanding of the requirements and opportunities for digital transformation has emerged across sectors. The fluid power domain, an essential part of many industrial systems, has likewise advanced efforts to develop Industry 4.0-compliant components and systems in recent years. This contribution provides a systematic overview of the current state of digital transformation in the fluid power, with a particular focus on Industry 4.0-concepts such as the Asset Administration Shell. The study offers a structured categorization of Industry 4.0-related research in the fluid power, mapping relevant publications and their thematic focus. Key use cases in which Industry 4.0-concepts are already integrated, including automated commissioning and simulation-based engineering, are presented in detail. The findings indicate significant progress in areas such as the integration of Industry 4.0-concepts into industry-relevant use cases, and the standardization of interfaces and component descriptions.
The hydraulic pitch system is one of the critical sub-systems of the wind turbine for both power regulation and also as part of the safety system by applying aerodynamic braking during the duration of extreme weather events. Various studies of wind turbine reliability have revealed that the hydraulic pitch system is one of the major contributors to the turbine’s downtime. Therefore, the focus of this study deals with the identification and mapping of failures in hydraulic pitch systems and the main components based on state-of-the-art failure mode knowledge and detection methods found in the literature. In this work, Fault Tree Analysis (FTA) is utilized to evaluate failures all the way down to root causes of major hydraulic components, i.e., on-off solenoid valves, proportional valves, hydraulic cylinders, and sensors used in hydraulic pitch systems. This facilitates a comprehensive understanding of failure modes and root causes within these hydraulic components. Nevertheless, the focus of this study has hence been to identify the methods to enhance fault detection and predictive maintenance strategies, ultimately improving the reliability and efficiency of hydraulic systems across various applications.
Adjustment of hydraulic motor displacement plays a critical role in regu-lating hydraulic circuits across a wide range of applications. However, the absence of commercially available continuously variable low-speed high-torque (LSHT) motor architectures poses a challenge. To evaluate a potential solution, this paper presents the dynamic analysis of an adjustable low-speed high-torque motor, the variable displacement linkage motor, with an emphasis on understanding the dynamics at different operating conditions. The hydraulic motor studied consists of five phase-shifted cam-linkage mech-anisms that can be dynamically adjusted to change the displacement within certain limits. The primary contribution of this paper is the mathematical modelling of the kinematics and dynamics of the adjustable linkage mecha-nism to explore the relative impact of the inertial, friction, and pressure-based forces. The results of the dynamic analysis reveal that inertial forces are higher when decreasing the displacement vs. increasing the displacement.Furthermore, at an operating pressure of triangle p = 22 MPa, there is a notable 47.1% increase in actuation force when the settling time is reduced from 150 to 25 ms. Additional analyses cover the influence of inertial forces on actuator force at various operating pressures, and the adjustment actuator flow rate requirements associated with different adjustment times.
Offshore winches commonly use conventional hydraulic drives, which are characterized by low energy efficiency. Digital displacement motors have shown promise for improving the energy efficiency of winch drive applications as they utilize digital valves for their operation. This paper compares the performance of a novel digital hydraulic winch drive and a conventional hydraulic winch drive with respect to their ability to control the load position of a commercial offshore knuckle-boom crane accurately. The considered digital winch drive consists of a digital displacement motor that operates in parallel with an electric motor. The power rating of the electric motor is small compared to that of the hydraulic motor, and its role is to smooth out the torque output of the digital displacement motor. The analysis shows that a smoother torque output reduces valve switchings and, therefore, increases the digital displacement motor’s volumetric efficiency. The drives’ performance is evaluated via simulations in four scenarios with varying conditions. The digital drive exhibits enhanced accuracy, achieving up to a 24 mm reduction in maximum load position error in three test scenarios, and performs comparably to the conventional drive in the fourth. Notably, the digital system controls the load with greater smoothness and fewer oscillations. These findings suggest that a digital displacement motor operating together with an electric motor presents a promising alternative for offshore winch drive applications.
Hydraulic systems are widely used throughout industry to actuate and control applications where large forces are required. These applications include off-highway machinery like excavators, loaders, manufacturing machinery like presses, injection molding machinery and so forth. With a continuously increasing focus on electrification and reduced energy consumption, emissions and rare earth material usage, energy efficiency, reduced component sizes and limited component numbers become increasingly important. In this endeavor, the recently introduced concept of hydraulic drive networks appears especially feasible to consider in applications with two or more hydraulic actuators to be controlled. Key features of hydraulic drives networks are the sole use of displacement units as flow control elements, absence of traditional control valves, and short-circuiting of hydraulic actuator chambers, while maintaining the possibility of individual control of each actuator. A consequence of these features is that possible ways of connecting the flow ports of displacement units to those of the actuators increases exponentially with the number of actuators to be controlled, rendering the use of traditional hydraulic system design methods obsolete. A possible way to systematically characterize and identify feasible networks is by use of graph theory. However, at this stage, no standardized approaches and definitions exist for such systems. This paper considers the concept of hydraulic drive networks in the framework of graph theory and applies the concept of tree-graphs to define the design space of feasible hydraulic drive network architectures for any number actuators constituting a number of control volumes for which flow must be controllable. Identifying and distinguishing each architecture in the design space is vital in the process of hydraulic drive network design, for being able to compare and optimize architectures based on objectives such as size, cost, efficiency and so forth.
This paper proposes a novel method for dimensioning pneumatic cylinders for motion tasks. It considers conventional downstream throttled pneumatic cylinders. The proposed approach is based on the maximal loading capacity of the end-cushion and the resulting formula for the dimensioning of the cylinder size has a simple algebraic structure. The method was experimentally validated showing great accuracy in estimating the motion time of optimally operated pneumatic cylinders.
