Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification Article Swipe
Jinsong Yang
,
Yizhen Peng
,
Jingsong Xie
,
Pengxi Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22124549
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22124549
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is difficult to monitor in real-time. Meanwhile, external uncertainties significantly impact bearing degradation. Therefore, this paper proposes a new bearing RUL prediction method based on long-short term memory (LSTM) with uncertainty quantification. First, a fusion metric related to runtime (or degradation) is proposed to reflect the latent degradation process. Then, an improved dropout method based on nonparametric kernel density is developed to improve estimation accuracy of RUL. The PHM2012 dataset is adopted to verify the proposed method, and comparison results illustrate that the proposed prediction model can accurately obtain the point estimation and probability distribution of the bearing RUL.
Related Topics
Concepts
Bearing (navigation)
Kernel density estimation
Computer science
Degradation (telecommunications)
Kernel (algebra)
Process (computing)
Metric (unit)
Nonparametric statistics
Reliability engineering
Artificial intelligence
Data mining
Engineering
Statistics
Mathematics
Telecommunications
Estimator
Operations management
Operating system
Combinatorics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22124549
- https://www.mdpi.com/1424-8220/22/12/4549/pdf?version=1655384494
- OA Status
- gold
- Cited By
- 73
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283169343
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4283169343Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22124549Digital Object Identifier
- Title
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Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty QuantificationWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-16Full publication date if available
- Authors
-
Jinsong Yang, Yizhen Peng, Jingsong Xie, Pengxi WangList of authors in order
- Landing page
-
https://doi.org/10.3390/s22124549Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/22/12/4549/pdf?version=1655384494Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/22/12/4549/pdf?version=1655384494Direct OA link when available
- Concepts
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Bearing (navigation), Kernel density estimation, Computer science, Degradation (telecommunications), Kernel (algebra), Process (computing), Metric (unit), Nonparametric statistics, Reliability engineering, Artificial intelligence, Data mining, Engineering, Statistics, Mathematics, Telecommunications, Estimator, Operations management, Operating system, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
73Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 16, 2024: 28, 2023: 22, 2022: 7Per-year citation counts (last 5 years)
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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