scholarly journals A multi-fault diagnostic method based on acceleration signal for a hydraulic axial piston pump

2019 ◽  
Author(s):  
Paolo Casoli ◽  
Mirko Pastori ◽  
Fabio Scolari
Keyword(s):  
Diagnostic Method ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Acceleration Signal ◽  
Hydraulic Axial Piston Pump ◽  
Fault Diagnostic ◽  
Axial Piston

scholarly journals An Integrated Deep Learning Method towards Fault Diagnosis of Hydraulic Axial Piston Pump

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6576
Author(s):  
Shengnan Tang ◽  
Shouqi Yuan ◽  
Yong Zhu ◽  
Guangpeng Li
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Image Features ◽  
Learning Ability ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Intelligent Fault Diagnosis ◽  
Time Frequency ◽  
Hydraulic Axial Piston Pump ◽  
Axial Piston

A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.

Research on the Dynamics and Variable Characteristics of a Double-Swash-Plate Hydraulic Axial Piston Pump With Port Valves

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Pengcheng Qian ◽  
Zengqi Ji ◽  
Bihai Zhu
Keyword(s):  
Rotational Speed ◽  
Flow Distribution ◽  
Variable Method ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Working Pressure ◽  
Swash Plate ◽  
Working Principle ◽  
Hydraulic Axial Piston Pump ◽  
Axial Piston

Axial piston pumps with port valves are widely used in applications that require high pressure and high power. In the present research, a new type of double-swash-plate hydraulic axial piston pump (DSPHAPP) with port valves is presented. The structure and working principle of the pump are discussed, and the balance characteristics of the pump are analyzed. A mathematical model of the pump flow distribution mechanism considering the leakage is established, based on which the effects of centrifugal forces acting on the port valves, working pressure, and rotational speed on the flow distribution characteristics are studied. A new method of varying the displacement of the pump that changes the phase relation of the two swash plates is proposed, and the principle and regulating characteristics of the variable method are studied. A detailed analysis of the forces and moments acting on the cylinder and the bearing reaction forces is presented. Finally, the relationship between volumetric efficiency and working pressure, and rotational speed and variable angle, is presented. It is revealed through an analysis that the working principle of the pump is feasible, and that the variable method can meet the requirements of varying the displacement of the pump. The characteristics of static balance and dynamic balance of the double-swashplate pump have the advantage of reducing vibration and noise. The research results also show that the reasonable matching of the working pressure and rotational speed can increase the pump's working performance to its optimum level.

Research on the sliding bearing pair of water hydraulic axial piston pump

2013 ◽  
Vol 227 (9) ◽  
pp. 2049-2063 ◽  
Author(s):  
Fanglong Yin ◽  
Songlin Nie ◽  
Zhenghua Zhang ◽  
Xiaojun Zhang
Keyword(s):  
Piston Pump ◽  
Hydrodynamic Condition ◽  
Axial Piston Pump ◽  
Test Rig ◽  
Sliding Bearing ◽  
Noise Characteristics ◽  
Lubrication Condition ◽  
Hydraulic Axial Piston Pump ◽  
Water Hydraulic ◽  
Axial Piston

Sliding bearing pair is one of the important friction pairs within water hydraulic axial piston pump, which can result in significant influences on the pump’s performance. Generally, owing to the characteristics of low viscosity and poor lubrication of water, the sliding bearing will operate under condition of dry or mixed lubrication, leading to a severe adhesives wear and material softening. In order to investigate the flow field of the sliding bearing in hydrodynamic condition, the effects of the water film pressure distribution, load carrying capacity changing with radial clearance and width–radius ratio of the sliding bearing pair have been simulated through MATLAB. And a suitable material combination of the sliding bearing pair was selected though a custom-manufactured friction and wear test rig. Based on the theoretical and experimental studies, an appropriate structure of the sliding bearing within water hydraulic axial piston pump was designed. The loading experiments for the developed water hydraulic axial piston pump assembled with two different flanges have been conducted at a water hydraulic component test rig. The experimental results revealed that the volumetric efficiency and noise characteristics of the pump are remarkably improved when the sliding bearing work under hydrodynamic lubrication condition in comparison with dry lubrication condition. The research results have laid the foundation for the development and improvement of the water hydraulic axial piston pump.

scholarly journals Intelligent Diagnosis towards Hydraulic Axial Piston Pump Using a Novel Integrated CNN Model

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7152
Author(s):  
Shengnan Tang ◽  
Yong Zhu ◽  
Shouqi Yuan ◽  
Guangpeng Li
Keyword(s):  
Working Environment ◽  
Piston Pump ◽  
Fault Classification ◽  
Axial Piston Pump ◽  
Time Frequency ◽  
System A ◽  
Automatic Information ◽  
Proposed Model ◽  
Hydraulic Axial Piston Pump ◽  
Axial Piston

As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing t-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.

Sign in / Sign up

Export Citation Format

Share Document