Fault diagnosis method for train bogie axlebox based on deep learning feature fusion of CWT-MVGG

Abstract

This paper addresses critical challenges in rail transit bogie fault diagnosis: strong noise interference, inadequate fault feature representation, and limited generalization in small-sample conditions. A deep learning-based fault diagnosis method is proposed, integrating variational modal decomposition (VMD), continuous wavelet transform (CWT), and a multi-level multi-scale feature fusion visual geometry group network (MVGG). The method utilizes a three-phase synergistic optimization framework. First, an adaptive VMD algorithm decomposes the original vibration signal into intrinsic mode functions. Second, CWT converts the one-dimensional time-series signal into a two-dimensional time-frequency image. Finally, the MVGG network is designed to incorporate multi-scale feature extraction, hybrid attention mechanisms, and multi-level feature fusion. Experimental results show that the proposed method achieves 98.98% fault diagnosis accuracy with 70% training samples. Compared with representative methods, it significantly enhances intra-class compactness and inter-class separability in the feature space. Notably, under 20% small-sample conditions, the method maintains 95.96% accuracy, demonstrating its parameter efficiency and strong generalization capability. Comprehensive analyses show that the proposed method exhibits superior noise robustness, enhanced feature discriminability, and strong small-sample adaptability in bogie axlebox bearing fault diagnosis. These findings offer a reliable solution for intelligent operation and maintenance of rail transit systems in complex operational environments.