Hydraulic Piston Pump Research Articles

METHODS OF RESEARCHING HYDRODYNAMIC AND RELATED PROCESSES IN STRUCTURES (REVIEW ARTICLE)

The article contains an analysis of publications on research methods of hydrodynamic and related processes in structures. In particular, attention is paid to centrifugal pumps. The existing technical solutions of these pumps are highlighted, as well as the operation of planetary hydraulic motors, where wear of the working surfaces of the rotors occurs. The problem of ensuring the stability of the dynamic characteristics of the hydraulic drive during transient processes using passive pulsation dampers is analyzed. An analysis of research devoted to the use of energy-efficient hydraulic motors in running modules of mechatronic systems of construction, road, utility, agricultural, railway and other self-propelled machines was carried out. The method of smoothing the working surfaces of the stator and rotor using computer modeling to eliminate the influence of the rotor on the stator during their interaction is considered. An analysis of the calculation and design of the optimal radial gap of the piston pair in the axial-piston pump was carried out. The dynamic model of cylinder block leakage and the lubricating film based on the lubrication of a pair of spherical valve plates for axial-piston pumps are analyzed. An analysis of the dynamic response of a two-tandem axial-piston pump with two-circuit flow control under pressure obstruction conditions was carried out. The diagnostic scheme for detecting malfunctions of the hydraulic piston pump was analyzed. For such heavily loaded machines as military, such connected models and research methods are necessary, as recommendations based on more adequate models are more grounded and perfect.

Evaluation of tribological behavior of tribopairs in water hydraulic radial piston pumps with multiple coatings and lubrication

Evaluation of tribological behavior of tribopairs in water hydraulic radial piston pumps with multiple coatings and lubrication

High-sensitivity and high-resolution triboelectric acoustic sensor for mechanical equipment monitoring

Traditional sensors for mechanical equipment monitoring often face challenges such as high cost, difficult installation and removal, and reliance on power sources. Herein, we introduced a polyvinylidene fluoride (PVDF) nanofiber membrane doped with BaTiO3 for constructing a triboelectric acoustic sensor (PB-TAS), innovatively designed for monitoring and recognizing the working conditions of mechanical equipment. This represents the first application of a triboelectric acoustic sensor for mechanical equipment monitoring. The PB-TAS demonstrates an ultra-broad frequency response range of 20–20,000 Hz, effectively covering the characteristic frequency spectrum (within 2000 Hz) during piston pump operation. It also exhibits extraordinarily sensitivity of 4.40 V Pa−1 at 85 dB, and ultra-high frequency resolution down to 0.1 Hz, enabling precise matching of the piston pump's acoustic characteristic frequencies. Integrated with deep learning techniques, the PB-TAS achieves real-time recognition of 15 distinct working conditions of a piston pump, with an impressive accuracy of 95.2 %. The PB-TAS, boasting exceptional sensitivity and frequency resolution, offers a cost-effective, miniaturized, self-powered, non-contact, and highly accurate solution for intelligent monitoring of hydraulic piston pumps.

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.

Noise optimization of hydraulic piston pump

With the development of electrification, the internal combustion engine (ICE) is gradually replaced by the electric motor. Lack of the ICE noise occultation, the noise of hydraulic piston pump becomes prominent. It is significant to decrease the noise of hydraulic piston pump for improving the driving comfort. The present study detailed investigates the simulation method for modal and noise calculation of the hydraulic piston pump. The noise prediction of hydraulic pump agrees well with measurements on the distribution of sound pressure vs. frequency. According to modal and noise distribution characteristics, the housing structure of hydraulic piston pump has been optimized to shift the modal frequency and decrease the noise. The simulation data proves that optimized hydraulic pump housing has great potential to decrease the noise. Compared with the original hydraulic pump housing, the sound pressure overall lever of optimized hydraulic piston pump is validated to decrease by 2-3dBA.

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.

SCAE-Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples.

Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time-frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability.

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.

Lubrication Modeling of the Reciprocating Piston with High Lateral Load and Various Conditions in a Swash Plate-Type Piston Pump

Most asymmetrical lateral forces occur in the reciprocating piston mechanism, which is widely applied as a major component of power equipment. When this lateral force greatly acts on the piston, it comes into contact with the cylinder. To prevent this negative phenomenon, lubrication characteristic evaluation and control technology are necessary. In this study, a boundary lubrication model considering the elastic deformation of the contact surface was adopted to perform a lubrication analysis of a piston hydraulic pump widely used in the aviation and plant industries. The piston/cylinder mechanism was analyzed in terms of contact force, characteristic thickness, and power loss while varying various design and operating parameters (friction coefficient, clearance, profiling shape, operating speed, and pressure). In the overall bearing capacity to withstand the tilt of the piston, the bearing capacity ratio due to contact at the interface increased more steeply than the bearing capacity ratio in the fluid lubrication area. Profiling of the piston head played a positive role in reducing power loss but also increased piston tilt. This trend appeared more clearly as the head profiling degree of processing Increased. Lastly, the effects of variable operating speed and pressure were examined. High operating speed caused low contact force, and high operating pressure caused high contact force. Through this study, it was possible to predict the lubrication performance and power loss of reciprocating piston pumps used in the field more realistically through appropriate boundary lubrication modeling.

A comparative investigation on wear behaviors of physical and chemical vapor deposited bronze coatings for hydraulic piston pump

The cylinder block/valve plate interface is one of the most critical frictional interfaces of the swashplate-type axial piston pump. However, the poor lubrication interface caused rapid wear and high friction loss in an elastohydrodynamic lubrication system, decreasing the pump lifetime. Wear resistant bronze coatings were fabricated on 38CrMoAl substrate by Physical Vapor Deposition (PVD) and Chemical Vapor Deposition (CVD), respectively. Ball-on-disc wear tests were performed to comparatively investigate the wear behaviors of the coatings and bulk ductile iron samples. It can be found that the PVD-bronze coating exhibited better wear resistance than the other two samples. This enhanced wear resistance was attributed to the unique composite microstructure and desired mechanical strength, which could resist to mechanical shear and spallation, decreasing friction loss. The appropriate hardness of (1.33 ± 0.07) GPa could be beneficial for enhancing its wear resistance. The PVD-bronze coating possessed a much lower and stable coefficient of friction (about 0.1) and wear rate (about 6000 μm3·N−1·m−1) under the loading forces of about 100 N after 20 min. The wear mechanism was the abrasive wear.

Design and experimental validation of a valve-distributed water radial piston pump

In underground coal mining, there is an urgent need for reducing the noise, hydraulic shock, and leakage of reciprocating pumps. Flow pulsation is strongly dependent on the number of pistons for single-acting reciprocating pumps. Water hydraulic radial piston pumps (RPPs) allow the pistons to be radially distributed, which makes the multi-piston design easier compared to traditional single-acting reciprocating piston pumps. In this study, a novel valve-distributed water RPP was developed in which the star wheel assembled on the camshaft is used to drive the connecting rod-crosshead-piston assembly and realize the reciprocation of the piston and suction-discharge of the pump check valves. Mathematical and numerical models representing the RPP were built using MATLAB and AMESim, respectively. Three-dimensional computational fluid dynamics analyses were performed to investigate the internal flow field of the suction and discharge circuits as well as the flow characteristics of the discharge valve opening. A prototype pump was manufactured and tested for 200 h and then disassembled to evaluate the wear condition. The tested instant flow rates were lower than those obtained from the theoretical and simulation analyses. The experimental results also revealed that the average flow rates were almost independent of the output pressure and that the volumetric efficiency decreased with increasing rotational speed when the output pressure was larger than 25 MPa. Inspecting the disassembled water RPP revealed that scuffing problems occurred in the pistons, whereas the other components remained intact. The findings of this study offer a reference for water hydraulic power units in various applications and for investigating the intensity of water hydraulics.

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.

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.

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.

A Data-Driven Diagnosis Scheme Based on Deep Learning toward Fault Identification of the Hydraulic Piston Pump

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump.

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.