Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Sheng Li
  • J. C. Ji
  • Yadong Xu
  • Ke Feng
  • Ke Zhang
  • Jingchun Feng
  • Michael Beer
  • Qing Ni
  • Yuling Wang

Research Organisations

External Research Organisations

  • Hohai University
  • UTS University of Technology Sydney
  • Nanjing University of Science and Technology
  • Xi'an Jiaotong-Liverpool University
  • University of Liverpool
  • Tongji University
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Details

Original languageEnglish
Article number111142
JournalMechanical Systems and Signal Processing
Volume210
Early online date23 Jan 2024
Publication statusPublished - 15 Mar 2024

Abstract

Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.

Keywords

    Complex working conditions, Dconformer, Fault diagnosis, Noisy scenarios, Rolling bearing, Vibration signal

ASJC Scopus subject areas

Cite this

Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults. / Li, Sheng; Ji, J. C.; Xu, Yadong et al.
In: Mechanical Systems and Signal Processing, Vol. 210, 111142, 15.03.2024.

Research output: Contribution to journalArticleResearchpeer review

Li S, Ji JC, Xu Y, Feng K, Zhang K, Feng J et al. Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults. Mechanical Systems and Signal Processing. 2024 Mar 15;210:111142. Epub 2024 Jan 23. doi: 10.1016/j.ymssp.2024.111142
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title = "Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults",
abstract = "Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.",
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AU - Ni, Qing

AU - Wang, Yuling

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