Unified feature learning network for few-shot fault diagnosis

Abstract

Few-shot fault diagnosis aims at diagnosing the state of mechanical signals with only a few training samples. Numerous contemporary approaches incorporate Time–Frequency Images (TFIs) derived from vibration signals to provide a thorough understanding of both the time and frequency domains. Current approaches often neglect the exploration of sample-level features, which hinders the enrichment and refinement of sample features. To this end, we propose a Unified Feature Learning Network (UFLN) designed to comprehensively model TFIs at two levels. We first present a Texture Enhancement Module (TEM) to amplify intricate details, aiding in the extraction of category-level features. Subsequently, we devise a dynamic Feature Selection Module (DFSM), tailored for the extraction of fault-related features. Finally, we develop an Intra-Diversity (ID) loss function to promote intra-class diversity, enriching the representation of each sample. Extensive experiments on three cases have demonstrated the effectiveness of the proposed UFLN approach.