Induction motor fault diagnosis method based on dual-attention mechanism

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (21) : 110-118.

SHI Jiancong, WANG Xinglong, ZHANG Jun

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Abstract

In the deep learning model based on multi-source data fusion, the features of different types of signals are usually mapped with equal proportion to the fusion layer. However, this process ignores the problem that the contribution of different signal features to the final recognition effect is inconsistent. For this reason, this paper proposes a deep learning model based on dual attention mechanism. In this model, firstly, the channel attention module is used to suppress the influence of irrelevant components in the homologous signal. Secondly, the multi-source data attention module is used to adaptively allocate the weight of non-homologous signal features, and then the re-weighted features are fused. Finally, the classifier is used to realize pattern classification. The proposed method is applied to the fault diagnosis of induction motor. The results show that the average recognition accuracy of this method is 99.74%, and its diagnosis effect is better than the existing methods.

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SHI Jiancong, WANG Xinglong, ZHANG Jun. Induction motor fault diagnosis method based on dual-attention mechanism[J]. Journal of Vibration and Shock, 2023, 42(21): 110-118

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