An Interpretable Few-Shot Framework for Fault Diagnosis of Train Transmission Systems with Noisy Labels

Abstract

Intelligent fault diagnosis technology plays an indispensable role in ensuring the safety, stability, and efficiency of railway operations. However, existing studies have the following limitations. 1) They are typical black-box models that lacks interpretability as well as they fuse features by simply stacking them, overlooking the discrepancies in the importance of different features, which reduces the credibility and diagnosis accuracy of the models. 2) They ignore the effects of potentially mistaken labels in the training datasets disrupting the ability of the models to learn the true data distribution, which degrades the generalization performance of intelligent diagnosis models, especially when the training samples are limited. To address the above items, an interpretable few-shot framework for fault diagnosis with noisy labels is proposed for train transmission systems. In the proposed framework, a feature extractor is constructed by stacked frequency band focus modules, which can capture signal features in different frequency bands and further adaptively concentrate on the features corresponding to the potential fault characteristic frequency. Then, according to prototypical network, a novel metric-based classifier is developed that is tolerant to mislabeled support samples in the case of limited samples. Besides, a new loss function is designed to decrease the impact of label mistakes in query datasets. Finally, fault simulation experiments of subway train transmission systems are designed and conducted, and the effectiveness as well as superiority of the proposed method are proved by ablation experiments and comparison with the existing methods.

Conflict of Interest Statement

The authors declare no conflicts of interest.