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Adversarial domain-invariant generalization: a generic domain-regressive framework for bearing fault diagnosis under unseen conditions

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
  • L. Chen
  • Q. Li
  • C. Shen
  • J. Zhu
  • D. Wang
  • M. Xia
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<mark>Journal publication date</mark>11/05/2021
<mark>Journal</mark>IEEE Transactions on Industrial Informatics
Number of pages10
Publication StatusE-pub ahead of print
Early online date11/05/21
<mark>Original language</mark>English

Abstract

Recently, various fault diagnosis methods based on domain adaptation (DA) have been explored to solve the problem of discrepancy between the source and target domains. However, given complex industrial scenarios, DA-based methods usually fail when the working conditions of machines are unseen, i.e., target data are unavailable during model training. In this work, a generic domain-regressive framework for fault diagnosis, namely, adversarial domain-invariant generalization (ADIG), is proposed. ADIG leverages multiple available domain data to exploit domain-invariant knowledge through adversarial learning between the feature extractor and the domain classifier. Simultaneously, the fault classifier generalizes the knowledge from the source-related domain to diagnose the unseen but related target domain signals. Moreover, customized strategies of feature normalization and adaptive weight are proposed to promote diagnosis performance. Comprehensive case studies show that ADIG achieves satisfactory diagnosis accuracy and robustness under unseen conditions, indicating that ADIG is a remarkably potential diagnosis tool for real-case industrial machines. IEEE

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©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.