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On improvement of a conditional mornitoring technique for condition-based maintenance
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2019 (Engelska)Ingår i: International Conference on Nuclear Engineering, Proceedings, ICONE, ASME Press, 2019Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

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.

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ASME Press, 2019.
Nyckelord [en]
Auto-associative kernel regression, Condition-based maintenance, Ensemble learning
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Identifikatorer
URN: urn:nbn:se:kth:diva-258163Scopus ID: 2-s2.0-85071394966ISBN: 9784888982566 (tryckt)OAI: oai:DiVA.org:kth-258163DiVA, id: diva2:1358051
Konferens
27th International Conference on Nuclear Engineering: Nuclear Power Saves the World!, ICONE 2019, 19 May 2019 through 24 May 2019
Anmärkning

QC 20191007

Tillgänglig från: 2019-10-07 Skapad: 2019-10-07 Senast uppdaterad: 2019-10-07Bibliografiskt granskad

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