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Multiple fault diagnosis for rolling bearings method employing CEEMD-GCN based on Horizontal Visibility Graph


 Multiple fault often occurs in the operation of rotating machinery transmission system. The fault signals of multiple bearings interfere with each other, which makes feature extraction and diagnosis of complex compound fault signals difficult. Because Graph Convolution Neural Network (GCN) can effectively map the structural information from complex data and its model has a certain generalization ability, this paper proposes a multiple fault diagnosis for rolling bearings method employing CEEMD-GCN based on Horizontal Visibility Graph. Firstly, in order to highlight the effective feature information in the multiple fault signal and reduce noise interference, multiple indicators of correlation and kurtosis are used to reconstruct the decomposed signals through complete ensemble empirical mode decomposition (CEEMD); Secondly, the reconstructed signals are constructed as Horizontal Visibility Graph (HVG), and HVG maps the time series signal to the graphic structure data reflecting the local geometric characteristics of the vibration signal through the horizontal visibility relationship; Finally, Taking the signal samples obtained by the HVG algorithm as the input data of the model, the graph convolution neural network model is trained to realize the diagnosis of multiple fault. The experimental results show that the presented methodology exhibits the superiority and generalization ability for multiple fault diagnosis.