An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

Jipu Li, Xiaoge Zhang, Ke Yue, Junbin Chen, Zhuyun Chen, Weihua Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In recent years, domain adaptation techniques have garnered significant attention in the field of intelligent fault diagnosis for mechanical equipment. Domain adaptation techniques can effectively enable the reuse of diagnostic knowledge, thereby improving the generalization performance of the trained model. However, many intelligent diagnostic models are tailored to specific diagnostic scenarios, including closed-set domain adaptation, partial domain adaptation, and open-set domain adaptation. When these models built for specific scenarios are applied to other scenarios, their diagnostic performance will deteriorate catastrophically. This study proposes an auto-regulated universal domain adaptation network (AUDAN) for uncertain diagnostic scenarios of rotating machinery, which can handle multiple diagnostic scenarios and adaptively set threshold boundaries for each fault category based on data distribution structure to distinguish known and unknown samples. Specifically, a novel integration loss function is designed to train the proposed AUDAN. The self-adaptive reweight module is introduced into the proposed method to distribute the relative weight for each loss function automatically. Experiments on two rotating machinery datasets are carried out for validations. The experimental results demonstrate that the proposed AUDAN performs effectively in uncertain diagnostic scenarios and is promising in addressing the fault classification tasks in real industries.

Original languageEnglish
Article number123836
Number of pages14
JournalExpert Systems with Applications
Volume249
Issue numberPart C
DOIs
Publication statusPublished - 1 Sept 2024

Keywords

  • Domain adaptation
  • Fault diagnosis
  • Rotating machinery
  • Transfer learning

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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