An improved RSMamba network based on multi-domain image fusion for wheelset bearing fault diagnosis under composite conditions

Abstract Numerous convolutional neural network (CNN)- and Transformer-based models have made significant progress in fault diagnosis. However, challenges remain, notably limited feature extraction abilities and elevated computational expenses, especially when applied to the fault diagnosis of wheelset bearings under intricate operational scenarios. This study proposes an improved RSMamba network based on multi-domain image fusion for wheelset bearing fault diagnosis. We have devised an RGB-CF strategy that integrates time, frequency, and time-frequency domain features to convert 1D vibration signals into 2D images. The RSMamba network is enhanced through the introduction of dynamic multi-path Mamba blocks, which handle non-causal relationships, and the embedding of a CSRA module to boost the model’s capacity to recognize class-specific features. The experimental results show that the proposed model attains a classification accuracy exceeding 99% across six testing tasks utilizing two distinct real-world wheelset bearing datasets, outperforming existing CNN- and Transformer-based models substantially in diagnostic accuracy and computational efficiency. This study demonstrates the substantial potential of the proposed methodology in enhancing fault diagnosis for wheelset bearings, making it a viable option for practical implementation in the maintenance of high-speed trains.