An induction motor fault diagnosis method based on the time-frequency image method and an improved graph convolutional network

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (24) : 241-248.

CHEN Qilei1, JIANG Yiyue1, TANG Yao2, ZHANG Xiaofei2, WANG Zhaohong1

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Abstract

The traditional fault diagnosis methods rely on the handcraft feature extraction, so its performance highly depends on the expert knowledge and its application is limited. To solve this problem, a fault diagnosis method based on time-frequency image method and improved Graph Convolutional Network (GCN) is proposed in this paper. Firstly, the vibration signals are transformed into time-frequency images by the wavelet transform method. Then, based on the simple linear iterative clustering (SLIC), the images are segmented adaptively to generate superpixels, which are regarded as nodes in the graph. Next, based on the color and texture features in the superpixels, the connection relationships and the node features are formed. Then, the graphs are input into the network to diagnose the fault type. The algorithm can extract the features automatically and make the diagnostic classification. To overcome the limitations in traditional GCN, the network is improved by the structure learning method. This method reconstructs the connect relationship by calculating the similarity between nodes, thereby keeping the structure integrity after pooling. By stacking the convolutional and pooling operations, the diagnosis can be realized in graph level. The results show that the proposed method can effectively classify different motor statuses under varying working condition, and the fault detection accuracy is the highest compared with the traditional deep learning method in the paper.

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CHEN Qilei1, JIANG Yiyue1, TANG Yao2, ZHANG Xiaofei2, WANG Zhaohong1. An induction motor fault diagnosis method based on the time-frequency image method and an improved graph convolutional network[J]. Journal of Vibration and Shock, 2022, 41(24): 241-248

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