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Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning.

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Title: Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning.
Authors: Liu, Sijie1 (AUTHOR) sijieliu_123@mail.nwpu.edu.cn, Li, Jin1 (AUTHOR) jin_li@mail.nwpu.edu.cn, Zhou, Nan1 (AUTHOR) xiguanan@mail.nwpu.edu.cn, Chen, Geng2 (AUTHOR) geng.chen@nwpu.edu.cn, Lu, Kuan3 (AUTHOR) lukuan@nwpu.edu.cn, Wu, Yafeng1 (AUTHOR) yfwu@nwpu.edu.cn
Source: Expert Systems with Applications. Jan2025, Vol. 260, pN.PAG-N.PAG. 1p.
Subjects: Supervised learning, Fault diagnosis, Transformer models, Machine learning, Feature extraction, Deep learning
Abstract: The precise identification of faults is an essential component in the effective operation and maintenance of rotating machinery (RM). The increasing adoption of deep learning in this field is likely due to the vast amounts of automatically generated monitoring data and its robust ability to learn and detect errors effectively. Deep learning techniques generally demonstrate high efficacy, particularly when limited fault data is available or when employing a two-stage diagnostic approach, involving manual feature extraction followed by fault classification. To address these challenges, we introduce the Expansive Dual-Attention Fusion Transformer (EDAF-Transformer), which is trained through a semi-supervised approach using minimal labeled data. Our method begins with a detailed explanation of the foundational theories behind transformers and semi-supervised learning strategies. This establishes the basis of our approach to intelligent fault diagnosis, particularly under the constraints of raw vibration signals and limited labeled data availability. We then present our novel model, the EDAF-Transformer , which comprises two main components: the Expansive Dual-Attention Enhanced Modules and the 1-D Twin Global Self-Attention Transformer Encoder Module. The first component employs an Expansive Dual-branch Attention Enhanced Module (EDAEM) within a dual-branch attention architecture. This design effectively enlarges the receptive field, capturing both global and local features. The second component, the Twin Sparse Self-Attention Fusion Module (TSSAFM), integrates Global Sparse Self-Attention with Squeeze-and-Excitation (SE) attention. This module is designed to enhance feature encoding capabilities while simultaneously reducing computational demands. Furthermore, we implement an uncertainty self-training semi-supervised strategy focused on balanced unlabeled sample selection. This approach significantly enhances the generalizability and performance of the EDAF-Transformer. We extensively tested our semi-supervised fault diagnosis method on publicly available motor bearing data and a specially curated transmission shaft dataset. The results from these tests demonstrate that our method surpasses other intelligent fault diagnosis methods in effectiveness. • Large kernels and multi-scale fusion boost vanilla Transformer in few-label IFD tasks. • Twin Sparse Self-attention fuses global and SE features, elevating IFD efficiency. • Uncertainty Bayesian deep learning SSL enhanced the proposed IFD methods. • Our model excels in IFD tests, besting 10 networks on CWRU and transmission shaft data. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: The precise identification of faults is an essential component in the effective operation and maintenance of rotating machinery (RM). The increasing adoption of deep learning in this field is likely due to the vast amounts of automatically generated monitoring data and its robust ability to learn and detect errors effectively. Deep learning techniques generally demonstrate high efficacy, particularly when limited fault data is available or when employing a two-stage diagnostic approach, involving manual feature extraction followed by fault classification. To address these challenges, we introduce the Expansive Dual-Attention Fusion Transformer (EDAF-Transformer), which is trained through a semi-supervised approach using minimal labeled data. Our method begins with a detailed explanation of the foundational theories behind transformers and semi-supervised learning strategies. This establishes the basis of our approach to intelligent fault diagnosis, particularly under the constraints of raw vibration signals and limited labeled data availability. We then present our novel model, the EDAF-Transformer , which comprises two main components: the Expansive Dual-Attention Enhanced Modules and the 1-D Twin Global Self-Attention Transformer Encoder Module. The first component employs an Expansive Dual-branch Attention Enhanced Module (EDAEM) within a dual-branch attention architecture. This design effectively enlarges the receptive field, capturing both global and local features. The second component, the Twin Sparse Self-Attention Fusion Module (TSSAFM), integrates Global Sparse Self-Attention with Squeeze-and-Excitation (SE) attention. This module is designed to enhance feature encoding capabilities while simultaneously reducing computational demands. Furthermore, we implement an uncertainty self-training semi-supervised strategy focused on balanced unlabeled sample selection. This approach significantly enhances the generalizability and performance of the EDAF-Transformer. We extensively tested our semi-supervised fault diagnosis method on publicly available motor bearing data and a specially curated transmission shaft dataset. The results from these tests demonstrate that our method surpasses other intelligent fault diagnosis methods in effectiveness. • Large kernels and multi-scale fusion boost vanilla Transformer in few-label IFD tasks. • Twin Sparse Self-attention fuses global and SE features, elevating IFD efficiency. • Uncertainty Bayesian deep learning SSL enhanced the proposed IFD methods. • Our model excels in IFD tests, besting 10 networks on CWRU and transmission shaft data. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=180885862
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.eswa.2024.125398
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 1
        StartPage: N.PAG
    Subjects:
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Transformer models
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Deep learning
        Type: general
    Titles:
      – TitleFull: Intelligent fault diagnosis of rotating machine via Expansive dual-attention fusion Transformer enhanced by semi-supervised learning.
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            NameFull: Liu, Sijie
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            NameFull: Li, Jin
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            NameFull: Zhou, Nan
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            NameFull: Chen, Geng
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            NameFull: Lu, Kuan
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            NameFull: Wu, Yafeng
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            – D: 15
              M: 01
              Text: Jan2025
              Type: published
              Y: 2025
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              Value: 260
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