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dc.contributor.advisorSerfontein, D.E.en_US
dc.contributor.advisorCilliers, A.C.en_US
dc.contributor.authorAyo-Imoru, R.M.en_US
dc.date.accessioned2020-11-05T07:10:18Z
dc.date.available2020-11-05T07:10:18Z
dc.date.issued2020en_US
dc.identifier.urihttps://orcid.org/0000-0002-4113-9637en_US
dc.identifier.urihttp://hdl.handle.net/10394/36188
dc.descriptionPhD (Nuclear Engineering), North-West University, Potchefstroom Campus
dc.description.abstractCondition-based maintenance (CBM) involves undertaking maintenance activities based on the health of the system. Establishing a CBM regime in an industry will result in eliminating unnecessary maintenance cost without jeopardising the safety of the plant. CBM has found useful applications in many industries like medicine, accounting, military, aeronautics, railway and many more. The nuclear power industry is also not completely left out. However, the nuclear power industry faces special challenges that make it more difficult to implement CBM, especially the unavailability of run-to-failure data. This thesis looked at the current practices of CBM in the nuclear industry and the ongoing research on the different methods and technologies being developed. Based on this, a hybrid system was developed to estimate the degradation level of a nuclear power plant (NPP) components. This should aid maintenance personnel in making useful decisions on the maintenance of NPP components. The hybrid system explored combining an NPP simulator and data-driven machine learning tools. The NPP simulator was used to generate the plant degradation data required for the machine learning tools. The machine learning tools of interest are the artificial neural network (ANN) and the neuro-fuzzy system. The thesis also estimated the requirements the simulator must meet in order to allow it to be used for plant diagnostics and prognostics. These include the user requirement specification (URS), the simulator model requirements and the functional requirements. Using data-driven methods for fault diagnostics and prognostics in the nuclear industry has been problematic because of the unavailability of run-to-failure data. This thesis has addressed this problem by employing an NPP simulator to generate the required data. The hybrid system used data from the simulator in training the ANN and the neuro-fuzzy system. The ANN was able to identify transients and faults while the neuro-fuzzy system was used in estimating the degradation level of the component. These results can be used by maintenance personnel in making informed decisions on whether or not to replace an NPP component.
dc.language.isoenen_US
dc.publisherNorth-West University (South Africa)en_US
dc.subjectArtificial neural network
dc.subjectcondition-based maintenance
dc.subjectdiagnostics
dc.subjectmachine learning
dc.subjectneuro-fuzzy system
dc.subjectsimulator
dc.subjecttransients
dc.titleA hybrid system for condition based maintenance in nuclear power plantsen_US
dc.typeThesisen_US
dc.description.thesistypeDoctoralen_US
dc.contributor.researchID10119582 - Serfontein, Dawid Eduard (Supervisor)en_US
dc.contributor.researchID11858176 - Cilliers, Anthonie Christoffel (Supervisor)en_US


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