Abstract

Accurate bearing fault diagnosis in electric drive systems is crucial for ensuring the safety and reliability of new energy vehicles. Aiming at the problem of inaccurate bearing fault diagnosis caused by the failure to fully utilize the time-frequency feature information of bearing faults and the lack of an adaptive selection mechanism for features, an intelligent bearing fault diagnosis method based on wavelet packet decomposition (WPD) time-frequency feature enhancement is proposed in this paper. First, the collected vibration signals are enhanced using WPD to obtain the full-frequency-band time-frequency information, which provides input for the bearing fault diagnosis model. Second, a hybrid neural network CNN-BiLSTM-AM for bearing fault diagnosis is constructed. On the basis of using the convolutional neural network (CNN) improved with cross-convolutional layers to extract multiscale spatial features of the input data and the bidirectional long short-term memory network (BiLSTM) to capture the bidirectional temporal dependence between features, the attention mechanism (AM) is introduced to adaptively weight and enhance key global features. Finally, a fully connected layer is employed to achieve intelligent classification of bearing fault states. Validation on a laboratory test dataset shows that the proposed method achieves an average diagnostic accuracy of 98.67%, outperforming existing benchmark models and exhibiting strong generalization ability. This study provides an effective and practical intelligent fault diagnosis scheme for bearings in electric drive systems.

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