A hierarchical transformer-based adaptive metric and joint-learning network for few-shot rolling bearing fault diagnosis

Abstract

Recently, deep learning techniques have significantly bolstered the advancement of intelligent fault diagnosis. However, in engineering practice, the limited availability of fault samples poses considerable challenges to the existing methods. To address this problem, a hierarchical Transformer-based adaptive metric and joint-learning network (HTAMJN) is suggested in this paper. Firstly, a hierarchical progressive fusion encoder based on orthogonal self-attention is devised, which effectively enhances the model’s attention to local features and establishes comprehensive interdependencies. To reduce the demand for labeled data, a metric method with a coupling mechanism is proposed to adaptively enhance the metric benchmark, making the category representation more discriminative. Additionally, a joint optimization scheme based on the episodic paradigm is formulated to improve the recognition capability of the network for different categories under few-shot scenarios. The outcomes demonstrate that the HTAMJN outperforms several comparison models in both few-shot diagnosis and cross-domain recognition tasks in experiments conducted on three bearing datasets, exhibiting superior performance.

Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm

“…To further improve the prediction efficiency of tool status, the machine learning (ML) driven based on multi-sensor signals (MSSs) have been studied by many scholars and constructed many reflection relationships between tool wear and signals [8][9][10]. For instance, Wang et al [11] constructed a system that can distinguish the wear state of various tools through a relevance vector machine.…”