Prototype matching-based meta-learning model for few-shot fault diagnosis of mechanical system

The efficacy of advanced deep-learning diagnostic methods is contingent mainly upon sufficient trainable data for each fault category. However, gathering ample data in real-world scenarios is often challenging, rendering these deep-learning techniques ineffective. This paper introduces a novel Prototype Matching-based Meta-Learning (PMML) approach to address the few-shot fault diagnosis under constrained data conditions. Initially, the PMML's feature extractor is meta-trained within the Model-Agnostic Meta-Learning framework, utilizing multiple fault classification tasks from known operational conditions in the source domain to acquire prior meta-knowledge for fault diagnosis. Subsequently, the trained feature extractor is employed to derive meta-features from few-shot samples in the target domain, and metric learning is conducted to facilitate swift and precise few-shot fault diagnosis, leveraging meta-knowledge and similarity information across sample sets. Moreover, instead of utilizing all target domain samples, the prototype of each fault category is used to capture similarity information between support and query samples. Concurrently, BiLSTM is employed to selectively embed the meta-feature prototype, enabling the extraction of more distinguishable metric features for enhanced metric learning. Finally, the effectiveness of the proposed PMML is validated through a series of comparative experiments on two fault datasets, demonstrating its outstanding performance in addressing both zero-shot and few-shot fault diagnosis challenges.