Few-shot fault diagnosis of switch machine based on data fusion and balanced regularized prototypical network

The turnout switch machine (TSM) is the critical signal equipment of the interlocking system, which directly affects the efficiency and safety of rail transit. However, the incomplete feature information of single-source data and the scarcity of fault data in real scenes make the existing methods unable to obtain satisfactory diagnostic results. Given the above issues, a few-shot fault diagnosis method based on data fusion and balanced regularized prototypical network (BRPN) is proposed. Firstly, a multi-signal adaptive fusion strategy is proposed to adaptively fuse three currents of the TSM to achieve a comprehensive and accurate expression of fault information. Secondly, a multi-level feature fusion network is designed to enhance the feature extraction ability of the fusion current signal. Furthermore, a prototype distribution balanced regularized strategy is proposed to balance the distribution differences between prototype interclass, so as to improve the diagnostic performance of the BRPN. Finally, the proposed BRPN model is verified by data fusion experiments and different small sample fusion datasets. Compared with other methods, the proposed method shows satisfactory diagnostic results in 1-shot and 5-shot experiments with two conversion processes, and the highest accuracy of fault diagnosis reaches 99.60%, which can provide a solution for solving the scarce fault samples of switch machines in a real-world situation.