Adaptive Attention-Driven Few-Shot Learning for Robust Fault Diagnosis

The deep learning techniques have propelled significant advancements in intelligent fault diagnosis. However, the limited labeled data due to resource-intensive labeling processes pose the challenges for actual applications. This study proposes an attention-centric model for few-shot fault diagnosis in rotating machinery. The model is informed by few-shot learning (FSL) and integrates internal and external attention (EA) mechanisms, which are leveraged to enhance the feature extraction capability. Performance evaluations under the five-way one-shot setting achieve remarkable results. The accuracy reaches 97.147% for the scenario from artificial damage to real damage, and 95.613% for the scenario of different operational conditions. The critical role of the integrated attention modules is further validated through the ablation study. Comparative analysis with state-of-the-art techniques demonstrates the superior performance of the proposed model. In short, this work provides an alternative method for fault diagnosis under the few-shot limitation.