基于双重注意力机制的异步电机故障诊断方法

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (21) : 110-118.

论文

施健聪，王兴龙，张俊

作者信息 +

Induction motor fault diagnosis method based on dual-attention mechanism

SHI Jiancong, WANG Xinglong, ZHANG Jun

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摘要

基于多源数据融合的深度学习模型中，通常采用等比重的方式将不同类型信号的特征映射至融合层。然而，该过程忽略了非同源信号特征对最终识别效果贡献程度不一致的问题。为此，本文提出了一种基于双重注意力机制的深度学习模型。该模型首先采用通道注意力模块抑制同源信号内无关分量的影响，其次利用多源数据注意力模块自适应分配非同源信号特征的权重，然后对重新赋权的特征进行融合，最后利用分类器实现模式分类。将所提方法应用于异步电机故障诊断，结果表明，该方法平均识别准确率为99.74%，其诊断效果优于现有方法。

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.

关键词

注意力机制 / 特征融合 / 深度学习 / 异步电机 / 故障诊断

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施健聪，王兴龙，张俊. 基于双重注意力机制的异步电机故障诊断方法[J]. 振动与冲击, 2023, 42(21): 110-118

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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