Research on Chebyshev Graph Convolutional Neural Network Modeling Method for Rotating Equipment Fault Diagnosis under Variable Working Conditions

Abstract

Given the challenges of rotating equipment fault diagnosis under variable working conditions, including the unbalanced transmission of information during feature extraction, difficulty in capturing both global and local features, and limited generalization across different working conditions, a Chebyshev graph convolutional neural network (ChebyNet) method is proposed to address these issues. First, a symmetry processing mechanism is incorporated into the framework of the ChebyNet to balance the transfer of information between nodes in the graph to ensure the fair and efficient integration of information. Secondly, the wide-area feature extraction capabilities of the ChebyNet and the adaptive nodes of the graph attention network (GAT) are integrated to achieve the comprehensive mining of fault characteristics and accurate characterization of complex interactive relationships. Finally, the node reconstruction task of self-supervised learning and collaborative node classification tasks are used to enhance the model’s ability to capture complex changes in variable working conditions data, significantly improving the generalizability of working conditions. In comparative and cross-validation experiments, the proposed method achieved an average diagnostic accuracy of 99.72%, representing an improvement of up to 17.96% compared to other graph neural network (GNN) models. It significantly enhances the accuracy, stability, and generalization of fault diagnosis. Ablation experiments further validate the effectiveness of the proposed method in improving fault diagnosis performance under variable working conditions.

Keywords

• fault diagnosis
• variable working conditions
• Chebyshev graph convolutional neural network
• multi-task collaboration