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

Zhe Wang, Yi Ding, Te Han*, Qiang Xu*, Hong Yan, Min Xie

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Citations (Scopus)

Abstract

The deep learning techniques have propelled significant advancements in intelligent fault diagnosis. However, the limited labeled data due to resource-intensive labeling processes poses 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 and integrates internal and external attention mechanisms, which are leveraged to enhance the feature extraction capability. Performance evaluations under the 5-way 1-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 demonstrate the superior performance of the proposed model. In short, this work provides an alternative method for fault diagnosis under the few-shot limitation. © 2024 IEEE.
Original languageEnglish
Pages (from-to)26034-26043
JournalIEEE Sensors Journal
Volume24
Issue number16
Online published9 Jul 2024
DOIs
Publication statusPublished - 15 Aug 2024

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 72201152, in part by the Research Grant Council of Hong Kong under Grant 11203519 and Grant 11200621, in part by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA), and in part by Sichuan Science and Technology Program under Grant 2023YFSY0003

Research Keywords

  • Attention mechanism
  • deep learning
  • fault diagnosis
  • few-shot learning (FSL)
  • rotating machinery
  • Sensors
  • Task analysis
  • Testing
  • Training

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