Hydraulic pump fault diagnosis of small samples based on an improved multi-graph convolutional network

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (24) : 59-67.

ZHENG Zhi，ZHAO Wenbo，LI Ke，ZHU Zhanhui，LIU Tongyao，SUN Yang，LIN Shuaiheng

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Abstract

Images can be converted into feature vectors by multi-graph convolutional network (M-GCN), and the aggregation of similar nodes can be enhanced by graph convolution operation. Due to the limitation of field space and economic conditions, it is impossible to collect sufficient fault samples of hydraulic pump, resulting the problem of small sample. When M-GCN is introduced to diagnose fault of hydraulic pump, there are problems such as insufficient discrimination and single information in the feature expression. Therefore, an improved multi-graph convolutional network, namely MMH-GCN, was proposed in this paper. In order to solve the problem of insufficient discrimination of model feature extraction, masked autoencoder (MAE) was introduced to reduce the encoding dimension and extract key image features, so as to improve diagnostic accuracy based on small sample size. For solving the problem of single feature information, the heterogeneous graph attention network (HAN) was introduced to extract much abundant and comprehensive features of graph structure data, so as to improve diagnostic accuracy and efficiency based on small sample size. Through the measured fault experimental verification and analysis of hydraulic pump, it can be seen that the MMH-GCN proposed in this paper has obvious efficiency and superiority compared with the original M-GCN, and the diagnostic accuracy and efficiency are increased by 12.14% and 14.63% respectively.

Key words

multi-graph convolutional network / masked autoencoder / heterogeneous graph attention network / small sample

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ZHENG Zhi, ZHAO Wenbo, LI Ke, ZHU Zhanhui, LIU Tongyao, SUN Yang, LIN Shuaiheng. Hydraulic pump fault diagnosis of small samples based on an improved multi-graph convolutional network[J]. Journal of Vibration and Shock, 2024, 43(24): 59-67

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