MIM-Graph: A multi-sensor network approach for fault diagnosis of HSR Bogie bearings at the IoT edge via mutual information maximization

The Internet of Things (IoT) is crucial in developing next-generation high-speed railways (HSRs). HSR IoT enables intelligent diagnosis of trains using multi-sensor data, which is critical for maintaining high speeds and ensuring passenger safety. Graph neural network (GNN)-based methods have gained popularity in HSR IoT research due to the ability to represent the sensor network as intuitive graphs. However, labeling monitoring data in the HSR scenario takes time and effort. To address this challenge, we propose a semi-supervised graph-level representation learning approach called MIM-Graph, which uses mutual information maximization to learn from a large amount of unlabeled data. First, the multi-sensor data is converted into association graphs based on their spatial topology. The unsupervised encoder is trained using global–local mutual maximization. The teacher–student framework transfers knowledge from the unsupervised encoder learned to the supervised encoder, which is trained using a small amount of labeled data. As a result, the supervised encoder learns distinguishable representations for intelligent diagnosis of HSR. We evaluate the proposed method using CWRU dataset and data from HSR Bogie test platform, and the experimental results demonstrate the effectiveness and superiority of MIM-Graph.