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Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization

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The traditional fault diagnosis method of industrial equipment has low accuracy and poor applicability. This paper proposes a equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization (RSAPSO). The entire model is validated by using the data of healthy bearings collected by Case Western Reserve University. Different gradient descent algorithms and standard particle swarm optimization ( PSO) algorithms in a back propagation (BP) network are compared experimentally. The results show that the RSAPSO algorithm has a higher accuracy of weight threshold updating than the gradient descent algorithm and does not easily fall into a local optimum. Compared with PSO, it has a faster optimization speed and higher accuracy. Finally, the RSAPSO algorithm is validated with the data of bearings collected from the laboratory rotating machinery test bench and motor data collected from the tower reflux pump. The average recognition rate of the four kinds of bearing data constructed is 97. 5% , and the average recognition rate of the two kinds of motor data reaches 100% , which prove the universality of the method.

fault diagnosisBP networkgradient descentPSO algorithm

Yang Jianjian、Zhang Qiang、Wang Xiaolin、Du Yibo、Wang Chao、Wu Miao

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Department of Mechanical, Electrical and Information Engineering, China University of Mining and Technology ( Beijing) , Beijing 100083 , China

Tian Di Science & Technology Co. , Ltd, Beijing 100013, China

National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesShanxi Special Fund for Science and Technology

20181010300612020YQJD0220181102027

2020

10.19682/j.cnki.1005-8885.2020.0033

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(4)
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