Fault diagnosis method driven by spatial-temporal features-based multi-round reconstructed GCN
WANG Qing-xin
ZHANG Xian-jie
ZHANG Hai-feng
ZHONG Kai
CHEN Hong-tian
HAN Min
Abstract：Recently,graph neural networks have been widely used to handle industrial process data with non-Euclidean structures.However,since the process data of equipment operation is often disturbed by noises and redundant information,the direct use of raw signal to construct a graph model will result in a less accurate graph structure,thus affecting the subsequent performance of the model diagnosis.A multi-round reconstructed graph convolutional network(GCN)fault diagnosis method that driven by spatial-temporal features(STMR-GCN)is proposed.The method firstly uses multi-scale convolutional neural network with GCN for feature extraction of fault signals.Then,graph structure will be reconstructed several times according to the cosine similarity between samples,and reconstructed graph model can reflect the connected edge relationship between samples more accurately.The obtained graph model is input to GCN to realize identification of fault types.Finally,experiments are conducted on Southeast University(SEU)simulation dataset and the real coal mill dataset,and experimental results show that the proposed method improves diagnosis accuracy compared with other comparative methods,which indicates the effectiveness and feasibility of STMR-GCN model in fault diagnosis.
Keywords：fault diagnosisspatial-temporal featuresmulti-round graph reconstructiongraph convolutional network
Publication Date：2025-01-27
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 149-157 )