MF-MSRNet: a fault diagnosis method for train bogie bearing based on multi-source data fusion and multi-scale residual network

As the core component of the railway train, the healthy state of the bogie bearing is essential for the safe operation of the train. The traditional bearing fault diagnosis methods typically rely on a single source signal, which cannot fully capture fault feature information, resulting in low diagnosis accuracy. To address this problem, this paper proposes a new diagnosis framework for train bogie bearings based on multi-source data fusion and multi-scale residual network (MF-MSRNet). Firstly, a multi-source data fusion method is designed to extract fault feature information from voiceprint, acoustic emission, and vibration sensor data, effectively extracting the low-dimensional features embedded in high-dimensional nonlinear data and fusing them into RGB images. Secondly, a new dual-scale residual block is presented to learn both profound and shallow features at various scales, thereby capturing bearing fault information in different spatial dimensions and enhancing the capacity to extract fusion features. Finally, to enhance the adaptability of the residual network in noisy scenes, a new denoising module is designed to help the network explore multi-scale features and filter irrelevant information. The experimental outcomes indicate that the classification accuracy of MSRNet in traction motor bearing datasets can reach up to 99.75%, and its comprehensive fault diagnosis performance is the best among all models.