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Adaptive multi-time scale integration of the high-speed train fault samples.

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Title: Adaptive multi-time scale integration of the high-speed train fault samples.
Authors: Liu, Suyan1,2 (AUTHOR), Wu, Chenxu2 (AUTHOR), Ma, Zengqiang1,2,3 (AUTHOR) mzqlunwen@126.com, Yuan, Zonghao3,4 (AUTHOR)
Source: Journal of Mechanical Science & Technology. Oct2024, Vol. 38 Issue 10, p5229-5240. 12p.
Subjects: Bootstrap aggregation (Algorithms), Particle swarm optimization, High speed trains, Prediction models, Data integration
Abstract: High-quality fault samples are regarded as the foundation of data-driven high-speed train bearing fault diagnosis. An adaptive multi-time scale fault sample integration method based on long short-term memory network (LSTM) is proposed to address the problem of insufficient multi-time scale fault samples during the degradation of bearing performance. First, we used particle swarm optimization (PSO) to extract multiple hyper-parameters from the LSTM network and constructed the PSO-LSTM prediction models, which can predict fault samples. Thereafter, we generated high-accuracy prediction PSO-LSTM models based on multiple time scales. Finally, multiple PSO-LSTM prediction models are integrated by using the Bagging algorithm. We compared the fault diagnostic rates of the integrated data model with the raw data using classic fault diagnosis algorithms. Experimental results indicated that the fault diagnostic rate of the integrated data is higher than that of the raw data. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature 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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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Mechanical+Science+%26+Technology%22">Journal of Mechanical Science & Technology</searchLink>. Oct2024, Vol. 38 Issue 10, p5229-5240. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Bootstrap+aggregation+%28Algorithms%29%22">Bootstrap aggregation (Algorithms)</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22High+speed+trains%22">High speed trains</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Data+integration%22">Data integration</searchLink>
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  Label: Abstract
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  Data: High-quality fault samples are regarded as the foundation of data-driven high-speed train bearing fault diagnosis. An adaptive multi-time scale fault sample integration method based on long short-term memory network (LSTM) is proposed to address the problem of insufficient multi-time scale fault samples during the degradation of bearing performance. First, we used particle swarm optimization (PSO) to extract multiple hyper-parameters from the LSTM network and constructed the PSO-LSTM prediction models, which can predict fault samples. Thereafter, we generated high-accuracy prediction PSO-LSTM models based on multiple time scales. Finally, multiple PSO-LSTM prediction models are integrated by using the Bagging algorithm. We compared the fault diagnostic rates of the integrated data model with the raw data using classic fault diagnosis algorithms. Experimental results indicated that the fault diagnostic rate of the integrated data is higher than that of the raw data. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Mechanical Science & Technology is the property of Springer Nature 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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        Value: 10.1007/s12206-024-0902-6
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        Text: English
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        Type: general
      – SubjectFull: Particle swarm optimization
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      – SubjectFull: High speed trains
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      – SubjectFull: Prediction models
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      – SubjectFull: Data integration
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            NameFull: Liu, Suyan
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            NameFull: Wu, Chenxu
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            – D: 01
              M: 10
              Text: Oct2024
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              Y: 2024
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