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

2021 ◽  
Vol 183 ◽  
pp. 108277
Author(s):  
Hao Pang ◽  
Defa Wu ◽  
Yipan Deng ◽  
Qian Cheng ◽  
Yinshui Liu
Keyword(s):  
Vibration Characteristics ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Noise And Vibration ◽  
Working Medium ◽  
Hydraulic Axial Piston Pump ◽  
Water Hydraulic ◽  
Axial Piston

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 Effect of damping groove on flow dynamics and vibration characteristics of axial piston pump

AIP Advances ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 035014 ◽  
Author(s):  
Tang Hesheng ◽  
Yan Ren ◽  
Jiawei Xiang ◽  
Cong Guo
Keyword(s):  
Vibration Characteristics ◽  
Flow Dynamics ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Dynamics And Vibration ◽  
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.

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