Few-shot fault diagnosis of turnout switch machine based on flexible semi-supervised meta-learning network

The safety of train operations hinges on the reliability of the signal system, and the switch machine stands out as a pivotal component within it. Consequently, fault diagnosis of switch machines is of paramount importance. However, obtaining a substantial amount of fault data is challenging in reality, and labeled data is even scarcer, which makes the fault diagnosis model of the switch machine have low diagnostic accuracy and poor generalization ability. To address these problems, a flexible semi-supervised meta-learning network (FSMN) is proposed for the fault diagnosis of switch machines in this paper. Firstly, a dual-channel hetero-convolution kernel feature extractor (DHKFE) is efficiently proposed to extract the switch machine fault features at different levels from fewshot samples. Secondly, a flexible distance prototype corrector is employed to adaptively modify the distance function. It accomplishes this by rapidly identifying similarities among fault samples and harnessing the potential of unlabeled data to fine-tune prototype positions, which can enhance prototype stability and generalization, leading to more accurate fault classification. Finally, the A-phase current data collected in the real scene during the transition between the two states of the switch machine are utilized for the validation of FSMN, alongside a comparative assessment against five other methods. The results show that the accuracy in forward-reverse and reverse-forward of FSMN is up to 97.35% and 92.72%, respectively, which means FSMN is superior in few-shot fault diagnosis and can be applied to various switch machines.