Show simple item record

dc.contributor.authorAyo-Imoru, R.M.
dc.contributor.authorCilliers, A.C.
dc.date.accessioned2018-04-18T12:18:55Z
dc.date.available2018-04-18T12:18:55Z
dc.date.issued2018
dc.identifier.citationAyo-Imoru, R.M. & Cilliers, A.C. 2018. Continuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power plant. Annals of nuclear energy, 118:61-70. [https://doi.org/10.1016/j.anucene.2018.04.002]en_US
dc.identifier.issn0306-4549
dc.identifier.issn1873-2100 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/26790
dc.identifier.urihttps://doi.org/10.1016/j.anucene.2018.04.002
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0306454918301828
dc.description.abstractA condition-based maintenance (CBM) regime in a nuclear plant will result in eliminating unnecessary maintenance cost without jeopardizing the safety of the plant. The foundation of a good CBM regime is an accurate and timely fault detection. A method has been developed to identify transients and detect fault in a Nuclear power plant in transients. This is to aid condition-based maintenance in a nuclear power plant. This method was achieved by using the nuclear plant simulator as a dynamic reference. At steady state, a fault is easily detected but in transients, it is difficult. This gives rise to the introduction of a machine-learning tool like artificial neural networks (ANN) to train both the simulator and plant parameters. The neural network outputs of the plant and simulator are then compared and this results in a better identification of faults in transientsen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectArtificial neural network (ANN)en_US
dc.subjectCondition-based maintenance (CBM)en_US
dc.subjectDynamic referenceen_US
dc.subjectFaulten_US
dc.subjectMachine learningen_US
dc.subjectTransienten_US
dc.titleContinuous machine learning for abnormality identification to aid condition-based maintenance in nuclear power planten_US
dc.typeArticleen_US
dc.contributor.researchID11858176 - Cilliers, Anthonie Christoffel
dc.contributor.researchID28103572 - Ayo-Imoru, Ronke Monica


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record