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On improvement of a...
On improvement of a conditional mornitoring technique for condition-based maintenance
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Li, W. (författare)
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Qi, Z. (författare)
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Ye, Y. (författare)
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visa fler...
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Yuan, Y. (författare)
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- Ma, Weimin (författare)
- KTH,Kärnkraftssäkerhet
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visa färre...
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(creator_code:org_t)
- ASME Press, 2019
- 2019
- Engelska.
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Ingår i: International Conference on Nuclear Engineering, Proceedings, ICONE. - : ASME Press. - 9784888982566
- Relaterad länk:
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https://urn.kb.se/re...
Abstract
Ämnesord
Stäng
- The condition-based maintenance (CMB) is a hot research topic to overcome the drawbacks belonging to the periodic maintenance used in nuclear power plants nowadays. Auto-Associative Kernel Regression (AAKR) is a widely applied condition monitoring technique which is the basis of a CBM. In this paper, the traditional AAKR is improved by using the ensemble learning technique. The modified AAKR is tested by steady-state operational data of a Tennessee-Eastman chemical process and the results show that it can significantly improve the auto- and cross-sensitivity without reducing the accuracy. This indicates a significant improvement in performance of this condition monitoring technique.
Ämnesord
- NATURVETENSKAP -- Fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences (hsv//eng)
Nyckelord
- Auto-associative kernel regression
- Condition-based maintenance
- Ensemble learning
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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