To improve the cross-working condition detection capability of the bearing fault diagnosis model,an OE-ACNN-BiGRU fault diagnosis method is proposed to optimize diagnosis performance by combining signal parity separation(OE),convolutional block attention module(CBAM),convolutional neural network(CNN)and bidirectional gating unit(BiGRU).First,the input sample signals are separated into odd and even ones,and convolution operations are performed respectively.Second,the CBAM attention module is used to apply spatial attention and channel attention mechanisms to the odd and even signals respectively.In addition,the processed odd and even signals are re-fused with features.Finally,a bidirectional gated recurrent unit is used to extract temporal features from the fused signal,and the detection and classification results are output after passing through the fully connected layer and softmax.The experiment results show that the detection accuracy reaches more than 99.66％.Compared with other ablation models,the average detection accuracy of the cross-working condition increases by more than 3 points to 94.36％.
维普
万方数据