A Fault Diagnosis Method for Rotating Machinery With Semi-Supervised Graph Convolutional Network and Images Converted From Vibration Signals

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In recent years, intelligent fault diagnosis has been widely investigated. Deep learning fault diagnosis methods for rotating machinery are primarily based on the assumption that labeled data are abundant. However, it is difficult to collect sufficient labeled samples for some practical engineering applications. To address this problem, a fault diagnosis method for rotating machinery with semi-supervised graph convolutional network (SSGCN) and two-dimensional (2-D) images converted from vibration signals is proposed in this paper. First, the one-dimensional (1-D) vibration signals are converted into 2-D images, by the no-parameter adjacency symmetrized dot pattern (NPASDP) method in polar coordinate system and the grayscale matrix mapping (GMM) method in Cartesian coordinate system. The local features of all samples are fully extracted using image processing methods in different coordinate systems to better characterize the relationship between different samples. Second, the normalized cross-correlation coefficient (NCC) is used to calculate the images similarity. Using each sample as a node, the graph structure is constructed by judging the adjacency relationship between all nodes through the similarity threshold. Finally, the SSGCN model is established to realize fault diagnosis. A bearing dataset and a rotor dataset with few data labels are used for validation, and the proposed method can get satisfactory fault diagnosis accuracy. The results show that the method can effectively improve the accuracy of model fault diagnosis with few labeled data.