A deep learning based health indicator construction and fault prognosis with uncertainty quantification for rolling bearings Article Swipe
Zhiyuan Wang
,
Junyu Guo
,
Jiang Wang
,
Yulai Yang
,
Lê Văn Đại
,
Cheng‐Geng Huang
,
Jia‐Lun Wan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ace072
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ace072
In this paper, a hybrid convolutional neural network (CNN)-bidirectional gated recurrent unit (BiGRU) model is integrated with the bootstrap method to endow the deep learning (DL) based prognostic method with the quantification capability of the prognostic intervals. The proposed hybrid method contains three parts: (I) The complete ensemble empirical mode decomposition with adaptive noise and principal component analysis and the CNN-BiGRU are utilized to automatically construct the health indicator (HI). (II) 3 σ criterion is employed to detect the first predicting time based on the HIs of rolling bearings. (III) The bootstrap method is imposed to endow the proposed DL method with the quantification capability of the prognostic intervals. The experimental validation is carried out on the XJTU-SY bearing dataset and the proposed method outperforms the other four methods in the majority of cases. In addition, the proposed method not only comprehensively considers the fault prognosis error caused by model parameters and noise, but also considers the prediction error caused by different combinations of features on the model.
Related Topics
Concepts
Hilbert–Huang transform
Computer science
Convolutional neural network
Artificial intelligence
Deep learning
Noise (video)
Fault (geology)
Pattern recognition (psychology)
Principal component analysis
Bearing (navigation)
Fault detection and isolation
Artificial neural network
White noise
Seismology
Telecommunications
Actuator
Image (mathematics)
Geology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ace072
- OA Status
- hybrid
- Cited By
- 28
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381618831
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381618831Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6501/ace072Digital Object Identifier
- Title
-
A deep learning based health indicator construction and fault prognosis with uncertainty quantification for rolling bearingsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-22Full publication date if available
- Authors
-
Zhiyuan Wang, Junyu Guo, Jiang Wang, Yulai Yang, Lê Văn Đại, Cheng‐Geng Huang, Jia‐Lun WanList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ace072Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1361-6501/ace072Direct OA link when available
- Concepts
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Hilbert–Huang transform, Computer science, Convolutional neural network, Artificial intelligence, Deep learning, Noise (video), Fault (geology), Pattern recognition (psychology), Principal component analysis, Bearing (navigation), Fault detection and isolation, Artificial neural network, White noise, Seismology, Telecommunications, Actuator, Image (mathematics), GeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
28Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 17, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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