Affiliation:
1. Key Laboratory of Conveyance and Equipment, East China Jiaotong University, Ministry of Education, Nanchang 330013, China
2. State Key Laboratory of Rail Transit Infrastructure Performance Monitoring and Guarantee, Nanchang 330013, China
Abstract
To overcome the problems of low machine learning fault diagnosis rate and long consumption time of deep learning in rolling bearing fault diagnosis, an RFR-GA-BLS model is proposed. The model is validated by infrared images of rolling bearings to find the most representative features, the most suitable parameters and the best diagnostic rate. Based on the pre-processed infrared thermal images of the faulty bearing, 72 second-order statistical features were obtained as information for fault diagnosis. RFR considered the robustness of the features, and new sequences were obtained. BLS was optimized by GA for fault diagnosis. New sequence features were added to the model sequentially, one at a time. After satisfying the model conditions, the most appropriate number of features was selected as the first 20. The search results for the number of feature nodes, the number of feature node windows and the number of enhancement nodes for the BLS were 24, 19 and 544, respectively, and the fault diagnosis rate of 98.8889% was achieved. According to a comparison with CFR-GA-BLS, BLS, PSO-BLS and Grdy-BLS, our proposed model is more advantageous in the search for the best performance. The fault diagnosis accuracy is higher compared to SVM and RF. The speed of our proposed model is 207 times faster than 1DCNN and 10,147 times faster than 2DCNN.
Funder
National Natural Science Foundation of China
Science and Technology Project of Jiangxi Provincial Department of Education
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference26 articles.
1. Review on Fault Diagnosis Methods for Rolling Bearings Based on Vibration Signals;Chen;Bearing,2022
2. Review of vibration signal processing methods;Li;Chin. J. Sci. Instrum.,2013
3. Overview of Acoustic Emission Fault Signal Analysis Method for Rolling Bearings;Tan;Guangdong Chem. Ind.,2015
4. Determination of rolling element bearing condition via acoustic emission;Cockerill;J. Eng. Tribol.,2016
5. Xu, Q., Huang, H., Zhou, C., and Zhang, X. (2021). Research on Real-Time Infrared Image Fault Detection of Substation High-Voltage Lead Connectors Based on Improved YOLOv3 Network. Electronics, 10.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献