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Early fault diagnosis strategy for high-speed train suspension systems based on model-agnostic meta-learning.

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Title: Early fault diagnosis strategy for high-speed train suspension systems based on model-agnostic meta-learning.
Authors: Yang, Funing1 (AUTHOR), Liu, Jikai1 (AUTHOR), Hua, Chunrong1 (AUTHOR) hcrong@swjtu.cn, Liu, Weiqun1 (AUTHOR), Dong, Dawei1 (AUTHOR)
Source: Vehicle System Dynamics. Oct2024, Vol. 62 Issue 10, p2510-2532. 23p.
Subjects: Fault diagnosis, Motor vehicle springs & suspension, High speed trains, Early diagnosis, Diagnosis
Abstract: Early fault diagnosis of suspension systems is essential for the safe operation of high-speed trains. However, neural network-based fault diagnosis methods have two remaining issues: the fault samples are difficult to obtain in practice and the early fault features are too weak to be extracted directly from the raw vibration signals using neural networks. A novel strategy is proposed for early faults diagnosis in suspension systems (i.e. component performance degradation within 20%) by integrating a new sample reconstruction method, a new grouping normalisation method, and model-agnostic meta-learning (MAML) algorithm. First, the 1D raw vibration signals are converted to 2D feature matrices consisting of artificial features using the sample reconstruction method; meanwhile, the grouping normalisation method is used to enhance the early weak fault features in the feature matrices. Second, MAML specialises in few-shot model training for early fault diagnosis, with the feature matrices as the training samples. Finally, the results are compared with those obtained using other current methods. The numerical results show that the proposed strategy yielded excellent performance in the few-shot early faults diagnosis of suspension systems, achieving a maximum accuracy of 94.75%. [ABSTRACT FROM AUTHOR]
Copyright of Vehicle System Dynamics is the property of Taylor & Francis Ltd 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
An: 179638552
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Early fault diagnosis strategy for high-speed train suspension systems based on model-agnostic meta-learning.
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  Data: <searchLink fieldCode="JN" term="%22Vehicle+System+Dynamics%22">Vehicle System Dynamics</searchLink>. Oct2024, Vol. 62 Issue 10, p2510-2532. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+vehicle+springs+%26+suspension%22">Motor vehicle springs & suspension</searchLink><br /><searchLink fieldCode="DE" term="%22High+speed+trains%22">High speed trains</searchLink><br /><searchLink fieldCode="DE" term="%22Early+diagnosis%22">Early diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Early fault diagnosis of suspension systems is essential for the safe operation of high-speed trains. However, neural network-based fault diagnosis methods have two remaining issues: the fault samples are difficult to obtain in practice and the early fault features are too weak to be extracted directly from the raw vibration signals using neural networks. A novel strategy is proposed for early faults diagnosis in suspension systems (i.e. component performance degradation within 20%) by integrating a new sample reconstruction method, a new grouping normalisation method, and model-agnostic meta-learning (MAML) algorithm. First, the 1D raw vibration signals are converted to 2D feature matrices consisting of artificial features using the sample reconstruction method; meanwhile, the grouping normalisation method is used to enhance the early weak fault features in the feature matrices. Second, MAML specialises in few-shot model training for early fault diagnosis, with the feature matrices as the training samples. Finally, the results are compared with those obtained using other current methods. The numerical results show that the proposed strategy yielded excellent performance in the few-shot early faults diagnosis of suspension systems, achieving a maximum accuracy of 94.75%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Vehicle System Dynamics is the property of Taylor & Francis Ltd 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.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/00423114.2023.2295935
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 2510
    Subjects:
      – SubjectFull: Fault diagnosis
        Type: general
      – SubjectFull: Motor vehicle springs & suspension
        Type: general
      – SubjectFull: High speed trains
        Type: general
      – SubjectFull: Early diagnosis
        Type: general
      – SubjectFull: Diagnosis
        Type: general
    Titles:
      – TitleFull: Early fault diagnosis strategy for high-speed train suspension systems based on model-agnostic meta-learning.
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            NameFull: Yang, Funing
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            NameFull: Liu, Jikai
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            NameFull: Hua, Chunrong
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            NameFull: Liu, Weiqun
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            NameFull: Dong, Dawei
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          Dates:
            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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              Value: 62
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              Value: 10
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            – TitleFull: Vehicle System Dynamics
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