A novel fault diagnosis model based on deep feature fusion network under imbalanced data: towards railway dual-switch machines traction occasion
Shuai Xiao, Qingsheng Feng, Xiaoxi Hu, Yakun Song, Guanglin Cong, Zhuoyi Yao, Hong Li
Abstract
Abstract Fault diagnosis of railway switch machines is crucial for ensuring safe and efficient train operations, as well as for the maintenance of intelligent Switching & Crossing systems. Current methods primarily focus on single-switch machine traction modes, often overlooking the challenges of effectively utilizing multi-source data and comprehensively representing fault information. This limitation results in restricted applicability and suboptimal recognition accuracy. To address these challenges, we propose a novel fault diagnosis model based on a deep feature fusion network (DFFN) specifically designed for railway dual-switch machines in traction occasion, particularly under imbalanced data conditions. First, we introduce an improved synthetic minority oversampling technique that integrates clustering technology with neighbor-based strategies to balance the experimental data and mitigate training bias. Second, we incorporate a cross-branch convolutional collaborative self-attention mechanism network and an adaptive weight learning network into the DFFN, facilitating the extraction of multi-scale fault feature correlations and promoting efficient fusion. Experimental results, based on multiple vibration sensing signals, demonstrate an average diagnostic accuracy of 96.66% and an F1-score of 96.85% in real railway environments. Comparative analyses with other state-of-the-art methods confirm that our approach achieves superior diagnostic performance.