Diagnosis of residual bidirectional LSTM automotive motor bearings with attention mechanism
In order to ensure the safe driving of vehicles and accurately diagnose and monitor motor bearing faults,this paper proposed an automotive motor bearing fault diagnosis method based on residual bidirectional Long Short-Term Memory(LSTM)network with attention mechanism.The feature extraction module combined LSTM groups that move in both forward and backward directions to fully perceive the fault features of automotive motor bearings.The signal diagnosis module adopted a residual bidirectional LSTM architecture and combined the local enhanced attention mechanism to optimize the weights and obtain the hidden state quantity.Global average pooling(GAP)method and SoftMax model are used in fault classification module to effectively detect faults.The results show that the detection accuracy of this method for automotive motor bearing fault detection reaches 93.1%.Under the condition of only 30 training samples,the accuracy reaches 66.3%.When the signal-to-noise ratio of the test set decreases from 10 dB to 2 dB,the accuracy only drops by 8.5%.Therefore,the proposed method has higher accuracy and stronger robustness.
vehicle safetymotor bearingsfault diagnosisattention mechanismfeature extractionaccuracy ratesignal-to-noise ratio
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