Graph neural network-based bearing fault diagnosis using Granger causality test

Detecting bearing faults helps ensure the healthy operation of machinery and prevents serious accidents. However, fault diagnosis method based on deep learning relies on the correlation between the extracted vibration signal features. It does not consider the causal relationship between fault, noise, and vibration waveform changes, resulting in lower bearing fault diagnosis accuracy under realistic working conditions. This study proposes a graph neural network (GNN) method based on the Granger causality test for bearing fault detection called GCT-GNN to address this issue. The proposed method first performs feature transformation on the original signals to extract time-domain and frequency-domain features, forming a feature matrix. Then, spectral analysis is conducted on both faulty signals with noise and healthy signals to calculate the lag order between them. Subsequently, the Granger causality test is employed to quantify the impact of faults and noise on signal changes, and the quantified results are used to calculate weights, constructing an adjacency matrix. The completed adjacency matrix and feature matrix are input into the GNN for feature mapping, last classifying the bearing fault data. In this paper, five models, Deep residual shrinkage Network (DRSN), GNN, Support Vector Machines(SVM), Random Forests (RF), and Convolutional Neural Network(CNN) were selected, and comparative experiments were carried out on two public data sets. The results show that, compared with other models, GCT-GNN has better anti-noise ability and is better than other models in various fault diagnoses.