NDLI: Fault Diagnosis of Rolling Bearing Based on BSA Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Du Wenliao Huang Chang Li Ansheng Gong Xiaoyun Wang Liangwen Wang Zhiyang
Copyright Year	2015
Description	Author affiliation: Sch. of Mech. & Electron. Eng., Zhengzhou Univ. of Light Ind., Zhengzhou, China (Du Wenliao; Huang Chang; Li Ansheng; Gong Xiaoyun; Wang Liangwen) \|\| Sch. of Mech. & Power Eng., Henan Polytech. Univ., Jiaozuo, China (Wang Zhiyang)
Abstract	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.
Starting Page	424
Ending Page	427
File Size	185647
Page Count	4
File Format	PDF
e-ISBN	9781467393232
DOI	10.1109/ISKE.2015.34
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Fault diagnosis fault diagnosis Neural networks Sociology Rolling bearings rolling bearing global optimal solution BSA based neural network Statistics Convergence
Content Type	Text
Resource Type	Article

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