Bearing Intelligent Fault Diagnosis Based on Edge Graph Attention Network
The data based on Euclidean space contains the relation information of nodes and edges,which has more information than the data in traditional Euclidean space.Howe ver,the traditional graph convolution and graph attention network focus on the extrac-tion of node information,while the edge information is not fully used.Aiming at this,by combining viewable algorithm and edge graph at-tention network(EGAT),irregular data based on non-Euclidian space were applied to bearing fault diagnosis.The diagnosis process was divided into two steps:the viewable algorithm was used to convert the original signal into graph data;EGAT was used to learn fault features,and then fault diagnosis could be carried out.The experimental results show that the graph convolutional network can achieve 100％accuracy in a single bearing fault classification task,which indicates that the proposed method has a distinct role in bearing fault diagnosis.
万方数据
维普
