A Gearbox Fault Diagnosis Method Based on Graph Neural Networks and Markov Transform Fields

Many current fault diagnosis methods tend to ignore the temporal correlation in signals, leading to a loss of critical fault information. Additionally, traditional diagnostic models often face challenges in terms of noise immunity, generalization, and handling non-Euclidean structured data. To address these issues, we propose a novel fault diagnosis approach that combines graph neural networks (GNNs) with the Markov transform field (MTF). We first use the MTF to convert vibration signals into 2-D images, preserving temporal correlation and preventing the loss of crucial fault information. Next, we use a graph convolutional neural network (GCN) to process graph-structured data, capturing global structural information. Finally, we introduce the graph attention network (GAT) to dynamically adjust node weights based on their relative importance, enhancing the overall model performance. In this article, we introduce a new fault diagnosis model, GCN-GAT, and evaluate it using the CWRU bearing dataset and a custom-built planetary gearbox dataset. The results show that our model maintains high fault detection accuracy even in the presence of significant noise and variable load conditions. This indicates that our approach demonstrates strong robustness and generalization, providing an effective solution for complex fault diagnosis tasks.