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Fault identificatio...
Fault identification with limited labeled data
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- Berenji, Amirhossein (författare)
- Shahid Beheshti University, Tehran, Iran
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- Taghiyarrenani, Zahra, 1987- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Rohani Bastami, Abbas (författare)
- Shahid Beheshti University, Tehran, Iran
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(creator_code:org_t)
- 2023-03-17
- 2024
- Engelska.
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Ingår i: Journal of Vibration and Control. - London : Sage Publications. - 1077-5463 .- 1741-2986. ; 30:7-8, s. 1502-1510
- Relaterad länk:
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https://urn.kb.se/re...
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visa fler...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Intelligent fault diagnosis (IFD) based on deep learning methods has shown excellent performance, however, the fact that their implementation requires massive amount of data and lack of sufficient labeled data, limits their real-world application. In this paper, we propose a two-step technique to extract fault discriminative features using unlabeled and a limited number of labeled samples for classification. To this end, we first train an Autoencoder (AE) using unlabeled samples to extract a set of potentially useful features for classification purpose and consecutively, a Contrastive Learning-based post-training is applied to make use of limited available labeled samples to improve the feature set discriminability. Our Experiments—on SEU bearing dataset—show that unsupervised feature learning using AEs improves classification performance. In addition, we demonstrate the effectiveness of the employment of contrastive learning to perform the post-training process; this strategy outperforms Cross-Entropy based post-training in limited labeled information cases. © The Author(s) 2023.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Autoencoder
- contrastive learning
- few-shot learning
- rolling element bearing
- Siamese network
- unsupervised feature learning
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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