Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization Article Swipe
Deqiang He
,
Jingren Yan
,
Zhenzhen Jin
,
Xueyan Zou
,
Sheng Shan
,
Zaiyu Xiang
,
Jian Miao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/tse/tdad021
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/tse/tdad021
Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy makes it difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on an improved long short-term memory (ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, a rectified linear unit (ReLU) is applied to activate a fully connected layer lying after the long short-term memory (LSTM) network, and the hidden state outputs of the layer are weighted by attention mechanism. The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/tse/tdad021
- https://academic.oup.com/tse/advance-article-pdf/doi/10.1093/tse/tdad021/50214462/tdad021.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4372202769
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4372202769Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/tse/tdad021Digital Object Identifier
- Title
-
Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-05Full publication date if available
- Authors
-
Deqiang He, Jingren Yan, Zhenzhen Jin, Xueyan Zou, Sheng Shan, Zaiyu Xiang, Jian MiaoList of authors in order
- Landing page
-
https://doi.org/10.1093/tse/tdad021Publisher landing page
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https://academic.oup.com/tse/advance-article-pdf/doi/10.1093/tse/tdad021/50214462/tdad021.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/tse/advance-article-pdf/doi/10.1093/tse/tdad021/50214462/tdad021.pdfDirect OA link when available
- Concepts
-
Hyperparameter, Computer science, Cross entropy, Bearing (navigation), Reliability (semiconductor), Artificial intelligence, Train, Artificial neural network, Machine learning, Pattern recognition (psychology), Geography, Cartography, Quantum mechanics, Power (physics), PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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