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

Authors :
Zhu, Yong
Zhou, Tao
Tang, Shengnan
Yuan, Shouqi
Source :
Journal of Marine Science & Engineering; Mar2023, Vol. 11 Issue 3, p616, 26p
Publication Year :
2023

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20771312
Volume :
11
Issue :
3
Database :
Complementary Index
Journal :
Journal of Marine Science & Engineering
Publication Type :
Academic Journal
Accession number :
162805099
Full Text :
https://doi.org/10.3390/jmse11030616