Abstract
Aim at the problem of large computation and low efficiency of traditional graph convolutional neural networks, a method of extracting bearing fault features based on graph wavelets is proposed. Graph wavelet has the advantages of sparsity and locality, which can provide higher efficiency and better interpretation for graph convolution. Firstly, the fault diagnosis signals of bearing are transformed into ring graph signals. The short bearing fault vibration segment signals are used as nodes, and a set of complete graph signals are formed by edge connection. Secondly, the compressed sparse row sparse matrix of the graph signal is calculated. Finally, the graph wavelet approach is used to extract defect features and classify bearing fault. The experimental results suggest that the graph wavelet-based bearing fault feature extraction approach has good pattern recognition and is a good method for automatic fault feature extraction and pattern identification.
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Acknowledgement
This research is a part of the research that is sponsored by the Wuhu Science and Technology Program (No. 2021jc1–6).
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Li, X., Li, H. (2022). Research on Bearing Fault Feature Extraction Based on Graph Wavelet. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_17
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