Fault diagnosis of rolling bearing based on BSA neural network

被引：0
|
作者：
Du Wenliao [1 ]
Huang Chang [1 ]
Li Ansheng [1 ]
Gong Xiaoyun [1 ]
Wang Liangwen [1 ]
Wang Zhiyang [2 ]
机构：
[1] Zhengzhou Univ Light Ind, Sch Mech & Elect Engn, Zhengzhou, Peoples R China
[2] Henan Polytech Univ, Sch Mech & Power Engn, Jiaozuo, Peoples R China
来源：
2015 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE) | 2015年
关键词：
BSA based neural network; rolling bearing; fault diagnosis; global optimal solution; SEARCH OPTIMIZATION ALGORITHM;
D O I：
10.1109/ISKE.2015.34
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the fault diagnosis programs, the intelligent algorithms, such as BP neural network, genetic neural network, ant colony neural network and so on, always suffer from the slow training speed and the local minimums. In this paper, a backtracking search optimization algorithm (BSA) based neural network was put forward, which used the BSA algorithm to train the neural network weights and thresholds. And then it was utilized on the pattern recognition of rolling bearing faults, and the results show that BSA neural network can better solve the problems of slow convergence and local minimums, which has a good application value.
引用
收藏
页码：424 / 427
页数：4
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