Fault Diagnosis Algorithm for Rolling Bearing Based on Convolution Graph Neural Network

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Integrated Circuits and Embedded Systems ›› 2022, Vol. 22 ›› Issue (4) : 38-41.

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Ding Shanshan, Chen Renwen, Liu Fei, Liu Hao

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Abstract

Aiming at the problem of low diagnostic accuracy of conventional deep learning methods when they directly processing one-dimensional time-domain vibration signals for rolling bearing fault diagnosis,a rolling bearing fault diagnosis algorithm based on one-dimensional convolutional neural network (CNN) and graph neural network (GNN) is proposed.First,perform adaptive data filtering and data compression preprocessing of the vibration signal through the one-dimensional convolutional neural network layers,then convert the preprocessed one-dimensional feature data into graph structure data,and in the end a three-layer graph neural network is used for fault diagnosis of rolling bearings.The experimental verification is carried out on the Case Western Reserve University Fault Rolling Bearing Data Set (CWRU).The results show that CGNN can have more than 90% accuracy of fault diagnosis under all operating conditions.

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graph neural network / convolutional neural network / deep learning / rolling bearing / fault diagnosis

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Ding Shanshan, Chen Renwen, Liu Fei, Liu Hao. Fault Diagnosis Algorithm for Rolling Bearing Based on Convolution Graph Neural Network[J]. Integrated Circuits and Embedded Systems. 2022, 22(4): 38-41

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