基于BiLSTM的滚动轴承故障诊断研究
Rolling bearing fault diagnosis based on BiLSM network
Aiming at rolling bearing fault diagnosis, a diagnosis model based on the bidirectional long short term memory (BiLSTM) network was designed and implemented. The original vibration signal was directly used as the input of the model and rolling bearing fault features were extracted automatically to do fault recognition of rolling bearings with different fault types and damage degrees of inner race, rolling element and outer race. The deep information of bearing vibration signals was extracted with BiLSTM network to make up for the deficiency of traditional fault diagnosis methods needing to extract features manually, and thus realize the end-to-end intelligent fault diagnosis of rolling bearing. The test results of rolling bearing really measured vibration signals showed that the fault recognition correctness rate of the proposed method can reach 99.8%; the proposed method has a certain application value.
双向长短期记忆网络 / 轴承故障诊断 / 深度学习 {{custom_keyword}} /
bidirectional long short term memory (BiLSTM) network / bearing fault diagnosis / deep learning {{custom_keyword}} /
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