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Author:

Cui, Lingli (Cui, Lingli.) | Wang, Gang (Wang, Gang.) | Liu, Dongdong (Liu, Dongdong.) | Pan, Xin (Pan, Xin.)

Indexed by:

EI Scopus SCIE

Abstract:

Domain adaptation (DA) techniques have significantly promoted the fault diagnosis of rolling bearings by leveraging diagnostic knowledge from a labeled source domain to recognize faults in an unlabeled target domain. However, dominant DA models often suffer from inaccurate estimation of distribution discrepancies. This stems from the fact that they perform domain alignment on a batch-by-batch basis, where the distribution discrepancies are evaluated solely using mini-batch data. In this paper, a novel dictionary domain adaptation transformer (DDAT) is proposed to boost cross-machine fault diagnosis of rolling bearings. First, a feature dictionary is constructed to represent domain attributes using multi-batch data, enabling more accurate estimation of the domain gap compared to existing batch-based methods. Second, a novel dictionary adaptation framework is designed to direct the model focus on inter-domain discrepancy instead of intra-domain variations caused by random sampling in data batches. Third, a domain-shared transformer feature extractor is developed to learn domain-invariant representations by leveraging the inherent advantages of multi-head attention in capturing long-range dependencies. The proposed DDAT method conducts domain adaptation at the dictionary level, benefiting from a more accurate estimation of distribution discrepancies by leveraging the abundant and diverse data in the dictionary. Experiments confirm that the proposed DDAT method outperforms the popular deep domain adaptation models in various cross-machine diagnosis tasks of rolling bearings.

Keyword:

Fault diagnosis Distribution discrepancy Domain adaptation Transformer Rolling bearings

Author Community:

  • [ 1 ] [Cui, Lingli]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Liu, Dongdong]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Gang]Beijing Univ Technol, Beijing Engn Res Ctr Precis Measurement Technol &, Beijing 100124, Peoples R China
  • [ 4 ] [Pan, Xin]Beijing Univ Chem Technol, Beijing Key Lab Hlth Monitoring & Selfrecovery Hig, Beijing 100029, Peoples R China

Reprint Author's Address:

  • [Liu, Dongdong]Beijing Univ Technol, Key Lab Adv Mfg Technol, Beijing 100124, Peoples R China;;

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Source :

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

ISSN: 0952-1976

Year: 2024

Volume: 138

8 . 0 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 19

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