Search Results (21)

To address the problem of tribological failure in an aircraft piston pump valve plate pair, the friction and wear properties of the valve plate pair materials (W9Mo3Cr4V-HAl61-4-3-1) of an axial piston pump at a certain speed and load were studied using a disc-ring tester under lubrication with No. 15 aviation hydraulic oil. The results show that the friction coefficient (COF) fluctuated in the range of 0.019~0.120 when the load (L) increased from 30 N to 120 N, and the speed increased from 100 r/min to 500 r/min. With the increase in the rotational speed, the COF of the valve plate pair decreased first and then increased. When the rotation speed (V) was 300 r/min, the relative COF was the smallest. Under L lower than 60 N, abrasive wear was the main wear mechanism. Under L higher than 90 N, the main wear mechanism was adhesive wear but mild oxidation wear also occurred. In addition, based on the V, L, radius (R), and test duration (T), which affected COF, the random forest regression (RFR) algorithm, the bagging regression (BR) algorithm, and the extra trees regression (ETR) algorithm were used as machine learning methods to predict the COF of the valve plate pair. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) were used to evaluate its performance, with the results showing that the ETR prediction model was the best method for predicting COF. The results of the machine learning also showed that the contributions of V, L, R, and T were 43.56%, 36.76%, 13.13%, and 6.55%, respectively, indicating that V had the greatest influence on the COF of the W9Mo3Cr4V/HAl61-4-3-1 friction pair. This study is expected to provide support for the rapid development of new valve plate pair materials. Full article

Measuring vibration velocity is one of the most common techniques to estimate the condition of industrial machines. At a constant operating point, as the vibration velocity value increases, the machine’s condition worsens. However, there are no precise thresholds that indicate the condition of a machine at different operating points. Also, the axial piston pump, which is the subject of the article, is a device that generates stronger vibrations by design and cannot be enclosed in general vibration norms. Due to different use cases and work regimes of axial piston pumps, the need to determine whether the device is working correctly for a broad spectra of operating points emerges. This article aims to present and compare different methods for vibration velocity prediction for axial piston pumps with use of neural networks including dense networks, variants of recurrent neural networks, and ensemble methods. The result of this research consists of models that have performance metrics that clearly indicate whether the monitored pump has malfunctioned or not across a wide variety of operating points, working conditions, and in case of reassembling. A detailed analysis of the influence of available measured variables on the performance of models is also provided. The conclusion is that the application of commercial implementation of developed models is reasonable in the context of both performance quality and costs of sensors needed to provide the necessary data. Full article

Axial piston pumps provide a number of benefits, including strong pressure resistance, high efficiency, high transmission power, a broad speed range, and a long lifespan. These characteristics have led to their widespread usage in naval applications, construction machines, and hydraulic machinery. On the other hand, axial piston pumps frequently display reduced operating speeds as well as instability because of their inherently nonlinear properties. In this study, a mathematical model of these changeable (variable) processes is developed using a mix of theoretical calculations and acquired data. An investigation of variable mechanism control in axial piston pumps is carried out centered on robust control methods, and the controller is constructed utilizing robust H_∞ theory. In terms of resilience, control precision, and system reaction time, simulations show that the H controller surpasses the PID controller. Full article

Positive displacement pumps produce pressure ripple that can be reduced with the attenuation of the generated flow ripple. This paper presents the application of a gas bladder hydraulic damper with the aim of reducing the oscillations of the delivery flow rate of positive displacement machines. This work is focused on the development of a 1D fluid dynamic model of the damper, which is based on the fundamental fluid motion equations applied for a mono-dimensional flow. In order to represent the fluid flow inside the damper, a particular evaluation of the sound speed has been implemented. Experimental tests have been performed involving an axial piston pump with the damper installed in the delivery pipe to validate the model; tests were carried out at different pump working conditions and with different gas precharge pressure of the damper. The test results confirmed the effectiveness of the device, and the comparison with numerical results demonstrated a good agreement. Simulations have been carried out to investigate the influence of various parameters on damper effectiveness. Full article

A water lubricating axial piston pump (WLPP) is the core power component of a green and environmentally friendly water hydraulic system. The friction and wear of the friction pairs of a WLPP are the key factors that restrict its development. In order to explore the friction and wear mechanism of materials, the tribological properties of CFRPEEK against 316L and 1Cr17Ni2 under water lubrication were investigated in a friction testing machine and an axial piston pump, respectively. An environmental scanning electron microscope (ESEM), confocal laser scanning microscopy and a surface profiler were used to analyze the morphology of the samples. In a friction testing machine, two different metals are paired with CFRPEEK, and the friction coefficient and wear rate barely show any differences. The wear rate of CFRPEEK is two orders of magnitude higher than that of metal. In the WLPP, 316L can hardly be paired with CFRPEEK, while 1Cr17Ni2 works well. The wear of 1Cr17Ni2 in the WLPP is greater than that of CFRPEEK. The high-pressure water film lubrication friction pairs cause the wear of the metal and show the difference in these two test methods. The wear mechanism is mainly abrasive wear. Improving the wear resistance of metals is very important for the development of WLPP. Full article

Hydraulic power systems are commonly used in heavy industry (usually highly energy-intensive) and are often associated with high power losses. Designing a suitable system to allow an early assessment of the wear conditions of components in a hydraulic system (e.g., an axial piston pump) can effectively contribute to reducing energy losses during use. This paper presents the application of a deep machine learning system to determine the efficiency state of a multi-piston positive displacement pump. Such pumps are significant in high-power hydraulic systems. The correct operation of the entire hydraulic system often depends on its proper functioning. The wear and tear of individual pump components usually leads to a decrease in the pump’s operating pressure and volumetric losses, subsequently resulting in a decrease in overall pump efficiency and increases in vibration and pump noise. This in turn leads to an increase in energy losses throughout the hydraulic system, which releases excess heat. Typical failures of the discussed pumps and their causes are described after reviewing current research work using deep machine learning. Next, the test bench on which the diagnostic experiment was conducted and the selected operating signals that were recorded are described. The measured signals were subjected to a time–frequency analysis, and their features, calculated in terms of the time and frequency domains, underwent a significance ranking using the minimum redundancy maximum relevance (MRMR) algorithm. The next step was to design a neural network structure to classify the wear state of the pump and to test and evaluate the effectiveness of the network’s recognition of the pump’s condition. The whole study was summarized with conclusions. Full article

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time–frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s₁₀, s₁₂, E_w(8), x₇, E_e(5), and E_e(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity. Full article

In recent years, the development of hydraulic variable displacement axial piston machines has been focused in two main directions: improvement of their construction and improvement of their displacement control methods. The goal of both directions is to increase the efficiency of the machines. Increasing their efficiency is key to improving the efficiency of the entire hydraulic system, whether they are used as pumps or hydraulic motors. This motivates the present work, which essentially contains a developed embedded control system designed for a known type of open circuit axial piston pump. The developed solution is implemented on a laboratory test rig. A detailed description of the hydraulic system in the context of pump displacement control is presented, as well as the developed system architecture for its control. The control system is based on a linear-quadratic Gaussian (LQG) controller. The controller is synthesized on the basis of a model obtained by means of identification based on experimental data. The designed controller is validated through experimental studies, enabling the analysis of its performance. Full article

The leakage of the tribological contact in axial piston pumps significantly impacts the pump efficiency. Leakage observations can be used to optimize the pump design and monitor the behavior of the tribological contact. However, due to assembly limitations, it is not always feasible to observe the leakage of each tribological contact individually with a flow rate sensor. This work developed a data-driven virtual flow rate sensor for monitoring the leakage of cradle bearings in axial piston pumps under different operating conditions and recess pressures. The performance of neural network, support vector regression, and Gaussian regression methods for developing the virtual flow rate sensor was systematically investigated. In addition, the effect of the number of datasets and label distribution on the performance of the virtual flow sensor were systematically studied. The findings are verified using a data-driven virtual flow rate sensor to observe the leakage. In addition, they show that the distribution of labels significantly impacts the model’s performance when using support vector regression and Gaussian regression. Neural network is relatively robust to the distribution of labeled data. Moreover, the datasets also influence model performance but are not as significant as the label distribution. Full article

Additive manufacturing (AM) of spare parts is going to become more and more common. In the case of hydraulic solutions, there are also some applications of AM technology related to topological optimization, anti-cavitation improvements, etc. An examination of all available research results shows that authors are using specialized tools and machines to properly prepare AM spare parts. The main aim of this paper is to analyze the influence of quick repair of the damaged slipper-retainer from an axial piston pump by using an AM spare part. Hence, it was prepared with a 100-h test campaign of the AM spare part, which covers the time between damage and supply of the new pump. The material of the slipper-retainer has been identified and replaced by another material—available as a powder for AM, with similar properties as the original. The obtained spare part had been subjected to sandblasting only to simulate extremely rough conditions, directly after the AM process and an analysis of the influence of the high surface roughness of AM part on wear measurements. The whole test campaign has been divided into nine stages. After each stage, microscopic measurements of the pump parts’ surface roughness were made. To determine roughness with proper measurements, a microscopical investigation was conducted. The final results revealed that it is possible to replace parts in hydraulic pumps with the use of AM. The whole test campaign caused a significant increase in the surface roughness of the pump’s original parts, which was worked with the AM spare slipper-retainer: (1) from Ra = 0.54 µm to Ra = 3.84 µm in the case of two tested pistons; (2) from Ra = 0.33 µm to Ra = 1.98 µm in the case of the slipper-retainer. Despite significant increases in the surface roughness of the pump’s parts, the whole test campaign has been successfully finished without any damages to the other important parts of the whole hydraulic test rig. Full article

In this study, sound measurements of an axial piston pump and an internal gear pump were performed and subjective pleasantness judgements were collected in listening tests (to analyze the subjective pleasantness), which could be seen as the inverse of the subjective annoyance of hydraulic drives. Pumps are the dominant sound source in hydraulic systems. The noise generation of displacement machines is subject of current research. However, in this research only the sound pressure level (SPL) was considered. Psychoacoustic metrics give new possibilities to analyze the sound of hydraulic drive technology and to improve the sound quality. For this purpose, instrumental measurements of the acoustic and psychoacoustic parameters are evaluated for both pump types. The recorded sounds are played back to the participants in listening tests. Participants evaluate them regarding the subjective pleasantness by means of paired comparison, which is an indirect scaling method. The dependence of the subjective pleasantness on speed and pressure was analyzed for both pump types. Different regression analyses were carried out to predict the subjectively perceived pleasantness or annoyance of the pumps. Results show that a lower speed is the decisive operating parameter for reducing both the SPL and the annoyance of a hydraulic pump. Full article

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives. Full article

The heat exchanger is widely applied to many axial piston machines, and its structure significantly affects the heat transfer performance. Flow characteristic and heat transfer performance in heat exchanger channels with different dimples geometries are numerically and experimentally analyzed in this research work. The objective is to present details of flow field structure and heat transfer mechanisms for the dimpled channel. The realizable k-ε turbulence model was employed in the numerical simulations with the Re range from 3500 to 20,000. The temperature contour, local streamlines, friction factor, and Nu were presented to illustrate the heat transfer enhancement mechanisms. From this investigation, it is found that dimples cause downward flow, improve the flow mixing and reattachment, interrupt the boundary layer and form periodic impingement flows and then greatly improve the heat transfer. The heat transfer coefficient of hemispherical dimple channels with the three kinds of dimple radius–depth ratios is the highest, and it is about 27.2% higher than that of the traditional rhombus dimple channel. Comparing to the rhombus dimpled channel, the lower flow friction performance of the hemispherical dimple channel depends on the lower dimple radius–depth ratio. The hemispherical dimpled channel present better overall thermal performance due to the strength and extent of the recirculation flow reduction. Full article

A novel virtual prototyping algorithm has been developed to design one of the most critical lubricating interfaces in axial piston machines of the swash plate type—the piston–cylinder interface—for operation with water as the working fluid. Due to its low viscosity, the use of water as a lubricant can cause solid friction and wear in these machines at challenging operating conditions. The prototyping algorithm compensates for this by tailoring the shape of the bore surface that guides the motion of each piston in this type of positive displacement machine to conform with the piston surface, taking into account both the piston’s tilt and its deformation. Shaping these surfaces in this manner can render the interface more conducive to generating hydrodynamic pressure buildup that raises its load-carrying capacity. The present work first outlines the structure of the proposed algorithm, then presents a case study in which it is employed to design a bore surface shape for use with two prototypes, one virtual and one physical—both modified versions of a 444 cc commercial axial piston pump. Experimental testing of the physical prototype shows it to achieve a significantly higher maximum total efficiency than the stock unit. Full article

This article presents a novel passive fluid borne noise source reduction strategy, based on tandem axial-piston unit indexing with the usage of symmetric lines. The strategy consists of setting the phase between the two synchronous units to accomplish destructive interference in targeted unit harmonics. A strategy capable of achieving destructive interference in all odd harmonics is investigated first analytically and then confirmed by a simulation study. Experiments on the proposed strategy confirmed its effectiveness at the first and third pump fundamental harmonics, and pressure ripple reduction was accomplished. The fluid borne noise source reduction in the first and third harmonic is verified to be propagated to pipe vibration and sound power. Regarding the first harmonic, pressure ripple was reduced by up to 18 dB; while for third harmonic, pressure ripple was reduced by up to 11 dB. In the experiment, however, noise cancellation is not achieved for the higher odd harmonics, as is instead found in the simulation. Conversely, transfer functions form pressure ripple to pipe wall acceleration are obtained experimentally, and a critical vibration band from 2000 Hz to 3000 Hz is identified as being crucial for effective overall sound power reduction. Full article