Hydraulic Axial Piston Pump Research Articles

SRSGCN: A novel multi-sensor fault diagnosis method for hydraulic axial piston pump with limited data

Deep learning has immense potential in ensuring the safe operation of hydraulic axial piston pumps (HAPP). However, the complex operating environment and high cost of labeling have resulted in a scarcity of labeled fault samples. This paper proposes a novel method called Siamese Random Spatiotemporal Graph Convolutional Network (SRSGCN). Firstly, based on graph convolutional networks, a Random Spatiotemporal Graph (RSG) is designed to aggregate multi-sensor information at different time stamps, fully exploiting the spatiotemporal features of the original data. Secondly, the Siamese Neural Network (SNN) is improved by retaining the twin subnetwork structure and removing the similarity output part. While preserving feature extraction capabilities, it is endowed with classification ability. Based on its strong feature mining capability, SRSGCN can fully utilize the scarce sample information to improve diagnostic accuracy. Finally, a case study was conducted on our HAPP experimental platform. The experiments show that compared with other existing methods, this method has higher diagnostic accuracy and can effectively solve the problem of HAPP fault diagnosis under limited data conditions.

Parallel quantized dual-level fully connected classifier for bearing fault diagnosis

The diagnosis of bearing failures in rotating mechanical equipment is critical to productivity and the quality of the manufacturing process. Traditional methods generally suffer from high human factor interference and low accuracy. This paper proposes a parallel quantized dual-level fully connected classifier (PQDFCC) for fault identification of bearings. The PQDFCC has two parts. The first part of the PQDFCC contains two types of data pre-processing. The first data pre-processing method is calculating absolute values and sorting the acquired raw signals. The second method of data pre-processing is quantizing the raw data. The second part of PQDFCC is a dual-level fully connected classifier. The first-level fully connected classifier performs the first-level fault classification on the quantized and sorted data; then, the output probability of the first-level classifier is considered as the input of the second-level fully connected classifier; finally, the second-level fully connected classifier provides the classification results of bearing faults. The data pre-processing part of the PQDFCC is a simple calculation that can simplify and not lose fault information; the dual-level fully connected classifier can fully exploit the hidden features of the data with high accuracy. The proposed PQDFCC is tested in the bearing failure, axial piston hydraulic pump, and self-priming centrifugal pump datasets; the experimental results show that the PQDFCC achieves 100% fault diagnosis accuracy.

Modeling load sensing pressure and flow control of axial piston pump by analyzing impact of bulk modulus

This research is focused on investigating the impact of the bulk modulus on the dynamics of variable delivery hydraulic axial piston pump (VAPP). The bulk modulus decreases exponentially with an increase in temperature whereas there is a linear positive relationship with pressure. The research revealed that there is a 6% (1 litre/s) increase in flow rate and a 2.6% (1.5 MPa) decrease in delivery pressure with a 38.75% (0.434 GPa) decrease in bulk modulus. Flow ripple and pressure pulsation are reduced by 39.3% and 43.2% respectively with a corresponding 38.75% decrease in bulk modulus. Pressure pulsation and flow ripple are responsible for the generation of noise and vibrations in the system. Flow rate increase contributes to better response and control of the VAPP. While a reduction in bulk modulus offers improved dynamic performance and overall response of the VAPP, it is noteworthy that a decrease in bulk modulus hurts the pump delivery pressure. The research allows the pump designer to formulate a strategy to optimize the bulk modulus under dynamic operating conditions to achieve optimal pump performance.

Bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features for bearing fault diagnosis

Among the existing bearing fault diagnosis algorithms, the diagnosis time of algorithms with high accuracy is relatively long, and the accuracy of some lightweight methods is low compared to other methods. This work proposes a bi-level binary coded fully connected classifier based on residual network 50 with bottom and deep level features (Bi-BUR) for bearing fault diagnosis, enabling both high accuracy and high-speed characteristics. The Bi-BUR has two levels of classifiers. The first level begins with a signal-to-picture method based on integer binary coding to convert the bearing fault signals into binary pictures; then, the first classification of the converted pictures is performed by the underlying feature extraction-residual network 50, which can extract both the underlying and the advanced features. The second level performs modal decomposition of the signals that are not classified successfully in the first level, performs the second classification with a fully connected classifier inserted into the underlying feature extraction module, and exports the final classification results after weighted summation of the results of each type. The Bi-BUR is simulated on the Case Western Reserve University motor, the self-priming centrifugal pump, and the axial piston hydraulic pump bearing failure datasets. All three experiments achieved more than 95% accuracy in the first level of categorization and 100% diagnosis after the second level of categorization. And the Bi-BUR is faster than other high-accuracy methods.

A light deep adaptive framework toward fault diagnosis of a hydraulic piston pump

The health condition of hydraulic axial piston pumps is crucial for the safety and reliability of hydraulic transmission systems. Diagnosis results of traditional methods indicate the high reliance on the experience, and current deep model-based methods are confronted with the difficulty of parameter tuning. A light adaptive deep framework is therefore constructed to reduce reliance of diagnosis on expert experience and still achieve the automatic screening of model hyperparameters. First, multi-sensor and multiple-channel signals of a piston pump are acquired for comprehensive raw data input. The raw one-dimensional signals are then converted into two-dimensional images using continuous wavelet transform. Then, a light deep model is built based on the convolutional operation and batch normalization techniques. The final model is obtained via global optimization of Bayesian algorithm. Next, the improved deep model is adopted for failure recognition of the essential components in the piston pump based on the transformed images. The average diagnosis accuracy achieves 97.46%, 98.71%, and 99.94% based on vibration signal, acoustic signal, and pressure signal respectively. The results reveal that the typical fault of the piston pump can be recognized intelligently and accurately with the proposed diagnosis method.

Failure analysis on the loose closure of the slipper ball-socket pair in a water hydraulic axial piston pump

The slipper ball-socket pair in the piston-slipper assembly is one of the key friction pairs in a Water Hydraulic Axial Piston Pump (WHAPP). The slipper ball-socket pair usually manufactured using a closing process to ensure its flexible swing and approximate zero axial clearance. Once the closure of the slipper becomes loose during use, resulting in excessive axial clearance, it will lead to pump failure. Similar problems occur on the WHAPP of the steel plate production line. In order to find out the root cause of the loose closure failure of the slipper ball-socket pair, the stress and strain during the closing process of the slipper ball-socket pair, the load during use, the size of the failed parts, and the macroscopic and microscopic morphologies were systematically characterized and analyzed. Through comprehensive failure analysis, the residual stress generated by the crimping and closing processes of the slipper ball-socket pair may be the main cause of its loosening and failure, while wear probable a secondary factor. Based on the conclusions, the direct injection molding method of the slipper ball-socket pair is proposed to solve this problem. The durability test results of the improved slipper ball-socket pair indicate that the countermeasures are effective. The obtained achievements would be helpful to improve the quality of the slipper closure and increase the service life of the WHAPP.

A Hydraulic Axial Piston Pump Fault Diagnosis Based on Instantaneous Angular Speed under Non-Stationary Conditions

Due to the intense noise interference in hydraulic systems, it is extremely difficult to detect component faults through vibration signals. Diagnostic performance is also constrained by highly time-varying and non-stationary operating conditions. This study proposes to use instantaneous angular speed (IAS) signals that are both operational and state parameters as sources of information. Firstly, the instantaneous angular speed fluctuation (IASF) of a piston pump is analyzed theoretically, and it is concluded that its fluctuating components contain the health status information of the components. The IASF can then be obtained by subtracting the speed trend term from IAS signals obtained via a magneto-electric speed sensor. A synchro-extraction of the normal S transform (SNST) is proposed to process it via line-pass filtering. Finally, the filtered and reconstructed IASF signal is utilized to draw a two-dimensional polar coordinate map online. A non-stationary-condition test is carried out on the test platform to monitor the morphological characteristics of the valve plate under normal, slight, and severe wear conditions. The polar plot shows significant increases in speed fluctuations and oscillation times within a range from 180° to 270°. The relevant research results reflect that the IAS signal can provide a new method for monitoring the operating status of and conducting fault diagnoses for hydraulic equipment.

Investigation on mixed thermalelstohydrodynamic lubrication behavior of slipper/swash plate interface in water hydraulic axial piston pump

To reveal the thermal-fluid-structure interaction characteristics of water-lubricated slipper/swash plate interface, a transient mixed thermoelstohydrodynamic (MTEHD) lubrication model incorporating asperity contact, elastic deformation, heat transfer and viscosity temperature effect is originally presented. The change in fluid properties caused by cavitation is considered for solution the piston chamber pressure. Distributions of pressure, temperature, deformation and tilting state of slipper simulated by MTEHD show strong reasonableness. Effect of operating and structural parameters on energy consumption characteristics calculated by MTEHD and hydrodynamic lubrication is contrastively analyzed. The results show that energy consumption is different in delivery and suction stroke due to different degrees of MTEHD effects and transient contact. Besides, the optimal dimension for the width of studied sealing surface is determined.

Effect of Material Selection and Surface Texture on Tribological Properties of Key Friction Pairs in Water Hydraulic Axial Piston Pumps: A Review

A water hydraulic axial piston pump has become the preferred power component of environmentally friendly water hydraulic transmission systems, due to its advantages of a compact structure, high power density, and so on. The poor friction and wear performance in the water medium, especially under extreme conditions of high speed and high pressure, limit the engineering application of the water hydraulic axial piston pump. In this review, the research progress for key friction pair materials (such as special corrosion-resistant alloys, engineering plastics, and engineering ceramics) for water hydraulic axial piston pumps is, firstly, summarized. Secondly, inspired by nature, the processing methods, lubrication drag-reduction mechanism, and tribological properties of the biomimetic surface textures are discussed. The effects of the surface texture shape, equivalent diameter, depth, and arrangement on the pump’s tribological properties are reviewed in detail. Finally, the application status of, and problems with, surface texture technology in water hydraulic axial piston pumps are summarized. It is suggested that future studies should focus on the multi-field coupling lubrication anti-friction mechanism of the multi-type composite texture under extreme conditions and mixed lubrication; and the anti-wear performance of the texture coupled with a coating modification, to further promote the surface texture in the field of lubrication antifriction engineering applications.

Wear prediction of YN/Si3N4 tribopair lubricated with seawater based on time-series prediction Group Method of Data Handling (GMDH) method

The time-series prediction Group Method of Data Handling (GMDH) method is proposed to predict the friction coefficient of YN9X/Si3N4 tribopair of seawater hydraulic axial piston pump based on wear experiment. The effects of surface roughness, YN nickel content, load, sliding speed and temperature on the friction and wear interaction and sensitivity of YN/Si3N4 tribopair in seawater were studied by orthogonal test. The influence of corrosion wear on the tribological properties of YN material was quantitatively analyzed. The experimental results show that YN9X has better tribological properties than other materials in artificial seawater. Raman spectrum analysis proves that there is tribological reaction between the friction pairs, which can effectively improve the tribological properties of the materials.

Failure Analysis and Intelligent Identification of Critical Friction Pairs of an Axial Piston Pump

Hydraulic axial piston pumps are the power source of fluid power systems and have important applications in many fields. They have a compact structure, high efficiency, large transmission power, and excellent flow variable performance. However, the crucial components of pumps easily suffer from different faults. It is therefore important to investigate a precise fault identification method to maintain reliability of the system. The use of deep models in feature learning, data mining, automatic identification, and classification has led to the development of novel fault diagnosis methods. In this research, typical faults and wears of the important friction pairs of piston pumps were analyzed. Different working conditions were considered by monitoring outlet pressure signals. To overcome the low efficiency and time-consuming nature of traditional manual parameter tuning, the Bayesian algorithm was introduced for adaptive optimization of an established deep learning model. The proposed method can explore potential fault feature information from the signals and adaptively identify the main fault types. The average diagnostic accuracy was found to reach up to 100%, indicating the ability of the method to detect typical faults of axial piston pumps with high precision.

A Novel Fault Diagnosis Method Based on SWT and VGG-LSTM Model for Hydraulic Axial Piston Pump

Since the hydraulic axial piston pump is the engine that drives hydraulic transmission systems, it is widely utilized in aerospace, marine equipment, civil engineering, and mechanical engineering. Operating safely and dependably is crucial, and failure poses a major risk. Hydraulic axial piston pump malfunctions are characterized by internal concealment, challenging self-adaptive feature extraction, and blatant timing of fault signals. By completely integrating the time-frequency feature conversion capability of synchrosqueezing wavelet transform (SWT), the feature extraction capability of VGG11, as well as the feature memory capability of the long short-term memory (LSTM) model, a novel intelligent fault identification method is proposed in this paper. First, the status data are transformed into two dimensions in terms of time and frequency by using SWT. Second, the depth features of the time–frequency map are obtained and dimensionality reduction is carried out by using the deep feature mining capability of VGG11. Third, LSTM is added to provide the damage identification model for long-term memory capabilities. The Softmax layer is utilized for the intelligent evaluation of various damage patterns and health state. The proposed method is utilized to identify and diagnose five typical states, including normal state, swash plate wear, sliding slipper wear, loose slipper, and center spring failure, based on the externally observed vibration signals of a hydraulic axial piston pump. The results indicate that the average test accuracy for five typical state signals reaches 99.43%, the standard deviation is 0.0011, and the average test duration is 2.675 s. The integrated model exhibits improved all-around performance when compared to LSTM, LeNet-5, AlexNet, VGG11, and other typical models. The proposed method is validated to be efficient and accurate for the intelligent identification of common defects of hydraulic axial piston pumps.

Failure analysis of auxiliary support bearing/shaft tribopair in seawater hydraulic axial piston pump

The auxiliary support structure is used to prevent the cylinder block from tilting in seawater hydraulic axial piston pump (SHAPP). The failure modes of auxiliary support bearing and shaft under different material combinations were compared. Experimental results show that improper design of radius fitting clearance in auxiliary support bearing/shaft tribopair will seriously affect the normal operation of SHAPP and even lead to its damage. Based on the inertia force, nonlinear water film force and dynamic load balance of the rotating cylinder block, the mathematical model of the nonlinear shaft centerline orbit of the auxiliary support bearing was established. The nonlinear shaft centerline orbit of auxiliary support bearing in the cylinder block bore during SHAPP operation is analyzed accurately. Simultaneously, the water film pressure field under different radius fitting clearance is simulated and the range of the optimal value is determined. The running-in experiment of SHAPP is carried out under the optimum clearance of the auxiliary support bearing/shaft tribopair. The results show that the SHAPP can run normally and the performance is obviously improved without the wear on the friction pair surfaces. Therefore, the proposed method has provided the guidance reference for the design of auxiliary support bearing in SHAPP.

A hybrid of CFD and PSO optimization design method of the integrated slipper/swashplate structure in seawater hydraulic axial piston pump

An integrated slipper/swashplate structure is developed for seawater hydraulic axial piston pump. Considering the overturning moment of the integrated slipper, the transient hydrodynamic lubrication model of the integrated slipper is established, and the body-fitted grid and five-point finite difference method is employed to accurately solve the model. An optimization design method based on a hybrid of CFD (Computational Fluid Dynamics) and PSO (Particle Swarm Optimization) for the integrated slipper is proposed for the interface structure with the low leakage and power loss. The optimal sealing belt of the integrated slipper is obtained, and the leakage and power loss are reduced by 27.3% and 16.7% under the same gap size, respectively. The effects of working condition on the film-forming characteristics and power consumption characteristics of the lubricating film are analyzed based on the optimized integrated slipper. It is found that the leakage and total power loss increase significantly with the increasement of working pressure and shaft speed, and decrease with the increasement of temperature. The excellent lubrication characteristics and strong anti-overturning ability of the integrated slipper/swashplate structure can improve the performance of the seawater axial piston pump, and its optimization design method proposed in this study can provide a new reference for engineers.

Experimental investigation and theoretical evaluation on the leakage mechanisms of seawater hydraulic axial piston pump under sea depth circumstance

Seawater hydraulic axial piston pump (SHAPP) is an important component of underwater hydraulic system. However, due to the particularity of the deep-sea high-pressure environment, the precise measurement of the SHAPP has not been carried out in the past, and there is a lack of performance evaluation of piston pump based on deep-sea test. In this paper, a testing system of the SHAPP in deep sea environment was designed and established, and the leakage mechanisms of the SHAPP is mainly studied by methods of experimental research and theoretical evaluation. The results indicated that the piston and piston bush produce micron deformation under sea depth circumstance. The compaction coefficient of plane pair (slippers/swashplate and port plate/valve plate) decreases gradually with the increase of sea depth. The main reasons why the leakage increases with the increase of sea depth under a certain load are that the clearance of the piston/piston bush becomes larger, and that the sealing of the plane pair gradually deteriorates due to the reduction of the compaction coefficient. Finally, the method of reducing leakage is given, this paper will provide theoretical and experimental reference for the design and application of the SHAPP in deep-sea environment.

Experimental Research of the Hydrodynamic Processes of an Axial Piston Water Hydraulic Pump

Water hydraulic components and systems play pivotal roles in the development of modern hydraulics. Sustainable development and environmental protection cannot be imagined without the use of water as a working medium in modern hydraulic systems. An axial piston pump is the main component of these systems. This paper presents analytical and experimental methods for the development of an axial piston pump. The presented mathematical model is rooted in numerous research results in the literature and in our own experience. It is based on mathematical modelling of hydrodynamic processes of a water hydraulic axial piston pump which, combined with experimental research, provides a great tool to analyse the influence of different factors on the operating process and to optimise the pump parameters. The experimental equipment used in our tests simulates a real hydraulic installation, and the obtained results are very close to the actual operating parameters. This research included a modification of the swash plate in order to achieve ideal operating parameters and, thus, extend the service life of the pump. Water has a number of advantages over conventional hydraulic oils. It is a sustainable, environmentally friendly resource, more readily available than oil (lower transportation costs), cheaper to dispose of, non-flammable and non-toxic, and its high thermal conductivity aids in cooling.

Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization

Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machinery since it is the vital component of fluid power systems. In the light of the undiscoverability of its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time–frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.

Intelligent fault diagnosis of hydraulic piston pump combining improved LeNet-5 and PSO hyperparameter optimization

The hydraulic axial piston pump is the power heart of the hydraulic transmission system in aerospace equipment and industrial filed. Its stable operation will directly affect the safety and reliability of the whole equipment. It is very significant to realize its health status monitoring and intelligent fault diagnosis. In view of the restrictions of traditional mechanical fault diagnosis in the dependence on a large number of signal processing technologies and expert diagnosis experience, as well as the time-consuming of data preprocessing, it is very meaningful to explore new ideas and methods to realize intelligent fault diagnosis of hydraulic piston pump. Based on the standard LeNet-5 model, the kernel size and kernel number are improved, and the batch normalization layers are added to the network architecture. Based on the Improve-LeNet-5 model, the recognition accuracy is chosen as the target value of the fitness function, the hyperparameters of the Improve-LeNet-5 model are automatically optimized via particle swarm optimization (PSO), including the learning rate, the number of convolution kernels, batch size, and the number of neurons in the fully connected layer. Finally, the PSO-Improve-CNN diagnostic model is constructed. And it is employed to classify and identify five signals data of hydraulic piston pump: normal state, swash plate wear, sliding slipper wear, loose slipper and center spring failure. Research result indicates that the recognition accuracy of PSO-Improve-CNN model can reach 98.71%, and the highest recognition accuracy can reach 99.06%, which are respectively higher than the standard LeNet-5 and Improve-LeNet-5 about 5.23% and 2.25%. By comparing with AlexNet, VGG11, VGG13, VGG16, and GoogleNet, the PSO-Improve-CNN model presents the highest diagnostic accuracy, less time in training and testing, and greater robustness. The comprehensive performance of the proposed model is demonstrated to be much stronger.

Effect of working medium on the noise and vibration characteristics of water hydraulic axial piston pump

Water hydraulic axial piston pump (WHAPP) is widely used in underwater vehicles. The noise and vibration of WHAPP are important for the concealment of underwater vehicles. In some hydraulic system, the oil and water inevitably permeate each other, resulting in changes of working medium characteristics and the vibration and noise of the WHAPP. In this study, the effect of working medium on the noise and vibration of WHAPP was researched through experimental and theory simulation methods. The noise and vibration are measured and compared when the WHAPP use different fluid (water or oil) as working medium. The results show that noise and vibration of WHAPP using oil as working medium are larger than that of using water as working medium, when the pressure is greater than 4 MPa. The simulation results show that the flow and pressure pulsation of oil is larger than that of water, which contributes to larger noise and vibration of oil. The differences of flow and pressure pulsation are mainly caused by the bulk modulus of working medium. This work also expresses that the effect of working medium characteristics must be considered for reducing the noise and vibration of axial piston pump.

Design of Check Valve System to the New Water Hydraulic Axial Piston Pump Driven by Cross Wobble Plate

Design of Check Valve System to the New Water Hydraulic Axial Piston Pump Driven by Cross Wobble Plate