1. Adaptive Attention-Driven Few-Shot Learning for Robust Fault Diagnosis
- Author
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Wang, Zhe, Ding, Yi, Han, Te, Xu, Qiang, Yan, Hong, and Xie, Min
- Abstract
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.
- Published
- 2024
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