Multiview Contrastive Shapelet Learning: A Novel Semi-Supervised Approach for Explainable Machine Fault Diagnosis With Insufficient Annotated Data

Abstract

Rotating machinery plays a vital role in modern industry, whose failures may cause sudden damage to the equipment and affect the reliability and safety of the whole mechanical system. Although numerous deep learning-based methods have emerged in industrial fault diagnosis, most of them suffer from two key limitations. First, the majority of these techniques are predicated on the assumption of abundant data availability. In practical industrial settings, however, labeled samples are limited, rendering these methods ineffective under such constraints. Second, a significant limitation of these intelligent methods lies in their lack of interpretability, which hampers their applicability in high-reliability fault diagnosis systems. To address these problems, this article proposes a multiview contrastive shapelet learning (MCSL) framework for semi-supervised fault diagnosis of rotating machinery. MCSL leverages both a supervised contrastive learning (SCL) module and a self-supervised contrastive learning (SSCL) module to comprehensively exploit labeled and unlabeled vibration signals. In SCL, a shapelet learner block is used to extract key explainable patterns from labeled vibration signals. Subsequently, the SCL algorithm is employed to minimize the feature distance between the original sequence and the extracted shapelets. In SSCL, several data augmentation techniques are first applied. Then, the augmented data are fed into the shapelet learner block. Furthermore, an interactive convolutional block is employed to extract multiscale features. The parameters of the MCSL model are updated within an integrated training framework. Through experimental validation utilizing both public and self-collected datasets, it is evident that MCSL not only outperforms state-of-the-art methods in diagnostic accuracy, but also demonstrates enhanced interpretability, underscoring its significant potential for industrial applications.