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

doi:10.19682/j.cnki.1005-8885.2020.0033

中国邮电高校学报(英文) ›› 2020, Vol. 27 ›› Issue (4): 17-25.doi: 10.19682/j.cnki.1005-8885.2020.0033

• Artificial Intelligence • 上一篇下一篇

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

杨健健张强王晓林杜毅博王超吴淼

中国矿业大学（北京）

收稿日期:2019-10-18 修回日期:2020-08-18 出版日期:2020-08-31 发布日期:2020-08-31
通讯作者: 杨健健 E-mail:yangjiannedved@163.com
基金资助:
国家自然基金;山西省重大专项;国家自然基金

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

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

Received:2019-10-18 Revised:2020-08-18 Online:2020-08-31 Published:2020-08-31

摘要/Abstract

摘要：

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.

关键词:

fault diagnosis, BP network, gradient descent, PSO algorithm

Abstract: 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.

Key words: fault diagnosis, BP network, gradient descent, PSO algorithm

中图分类号:

TP183

Yang Jianjian, Zhang Qiang, Wang Xiaolin, Du Yibo, Wang Chao, Wu Miao. Research on equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization[J]. The Journal of China Universities of Posts and Telecommunications, 2020, 27(4): 17-25.

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

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

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