Bearing Fault Diagnosis Method Based on EEMD and LSTM

Ping Zou, Baocun Hou, Jiang Lei, Zhenji Zhang

Abstract


The condition monitoring and fault detection of rolling bearing are of great significance to ensure the safe and reliable operation of rotating machinery system.In the past few years, deep neural network (DNN) has been recognized as an effective tool to detect rolling bearing faults. However, It is too complex to directly feed the original vibration signal to the DNN neural network, and the accuracy of fault identification is not high. By using the signal preprocessing technology, the original signal can be effectively removed and preprocessed without losing the key diagnosis information. In this paper, a new EEMD-LSTM bearing fault diagnosis method is proposed, which combines the signal preprocessing technology with the EEMD method that can get clear fault feature signals, and LSTM technology to extract fault features automatically that improves the efficiency of fault feature extraction. In the case of small sample size, this method can significantly improve the accuracy of fault diagnosis.

Keywords


fault diagnosis, EEMD, LSTM, motor bearing.

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[Online]. Available: www.phmsociety.org/competition/PHM/09, Accesed on 14 February 2019.




DOI: https://doi.org/10.15837/ijccc.2020.1.3780



Copyright (c) 2020 Ping Zou, Jiang Lei, Zhenji Zhang

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