Author:
Tan Wenwen,Sun Yuansheng,Qiu Dawei,An Yapeng,Ren Ping
Abstract
Abstract
In order to meet the real-time requirement of bearing fault diagnosis, a simplified strategy of the traditional long short term memory (LSTM) neural network structure is proposed. We designed a new single gated unite (SGU) recurrent neural network. In view of the non-stationary and non-linear characteristics of bearing fault vibration data, we use wavelet packet decomposition to extract the features as input signals of bi-directional single gated unite (Bi-SGU) to complete the diagnosis of 10 types of bearing data. Simulation results show that the proposed method can ensure the accuracy of bearing fault diagnosis, reduce the number of network parameters by 36%, and improve time efficiency by 41%.
Subject
General Physics and Astronomy
Reference14 articles.
1. Fault diagnosis of rolling bearings based on Marginal Fisher analysis [J];Jiang;Journal of Vibration and Control,2014
2. Bearing fault diagnosis based on a new acoustic emission sensor technique [J];Van;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability,2015
3. The application of time-frequency reconstruction and correlation matching for rolling bearing fault diagnosis [J];Xu;Proceedings of the Institution of Mechanical Engineers, Part C. Journal of mechanical engineering science,2015
4. A roller bearing fault diagnosis method based on EMD energy entropy and ANN [J];Yang;Journal of Sound and Vibration,2006
5. Rolling bearing fault diagnosis based on intrinsic mode function energy moment and BP neural network [J];Qin;Journal of Vibration, Measurement & Diagnosis,2008
Cited by
3 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