Intelligent fault diagnosis of rolling bearing using the ensemble self‐taught learning convolutional auto‐encoders Article Swipe
Yilan Zhang
,
Jinxi Wang
,
Faye Zhang
,
Shanshan Lv
,
Lei Zhang
,
Mingshun Jiang
,
Qingmei Sui
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1049/smt2.12092
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1049/smt2.12092
The lack of labelled data presents a common challenge in many fault diagnosis and machine learning tasks. It requires the model to be able to efficiently capture useful fault features from a smaller amount of labelled data. In this paper, a method to train multiple convolutional auto‐encoders by self‐learning method and integrate them using ensemble learning, called ensemble self‐taught learning convolutional auto‐encoders (STL‐CAEs), is proposed, which can effectively extract features of bearing vibration signals. First, an ensemble learning strategy is proposed to obtain two auto‐encoders that satisfy the strategy by optimizing the model parameters and structure. Then, a self‐taught learning training method is proposed to solve the problem of little label data. Finally, ensemble learning and fault diagnosis is achieved by the SoftMax classifier. Applying the proposed method to the bearing data from Case Western Reserve University, the STL‐CAEs have higher accuracy and generalization than common fault diagnosis methods such as CAE, CNN, SAE and EMD, and also have significant advantages in terms of diagnostic time and training time.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/smt2.12092
- OA Status
- gold
- Cited By
- 8
- References
- 34
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3217425010Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/smt2.12092Digital Object Identifier
- Title
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Intelligent fault diagnosis of rolling bearing using the ensemble self‐taught learning convolutional auto‐encodersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-24Full publication date if available
- Authors
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Yilan Zhang, Jinxi Wang, Faye Zhang, Shanshan Lv, Lei Zhang, Mingshun Jiang, Qingmei SuiList of authors in order
- Landing page
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https://doi.org/10.1049/smt2.12092Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1049/smt2.12092Direct OA link when available
- Concepts
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Bearing (navigation), Fault (geology), Computer science, Encoder, Artificial intelligence, Ensemble learning, Autoencoder, Pattern recognition (psychology), Deep learning, Operating system, Geology, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2, 2023: 3, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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