Support vector ensemble for incipient fault diagnosis in nuclear plant components
The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose...
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doaj-8008c25d1f5946249c7f557726246dad2020-11-25T00:45:00ZengElsevierNuclear Engineering and Technology1738-57332018-12-0150813061313Support vector ensemble for incipient fault diagnosis in nuclear plant componentsAbiodun Ayodeji0Yong-kuo Liu1Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin, Heilongjiang 150001, China; Nuclear Power Plant Development Directorate, Nigeria Atomic Energy Commission, Abuja, NigeriaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin, Heilongjiang 150001, China; Corresponding author.The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper. Keywords: Fault diagnosis, Support vector machine, Error correcting output code, Reactor coolant systemhttp://www.sciencedirect.com/science/article/pii/S1738573317306988 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Abiodun Ayodeji Yong-kuo Liu |
spellingShingle |
Abiodun Ayodeji Yong-kuo Liu Support vector ensemble for incipient fault diagnosis in nuclear plant components Nuclear Engineering and Technology |
author_facet |
Abiodun Ayodeji Yong-kuo Liu |
author_sort |
Abiodun Ayodeji |
title |
Support vector ensemble for incipient fault diagnosis in nuclear plant components |
title_short |
Support vector ensemble for incipient fault diagnosis in nuclear plant components |
title_full |
Support vector ensemble for incipient fault diagnosis in nuclear plant components |
title_fullStr |
Support vector ensemble for incipient fault diagnosis in nuclear plant components |
title_full_unstemmed |
Support vector ensemble for incipient fault diagnosis in nuclear plant components |
title_sort |
support vector ensemble for incipient fault diagnosis in nuclear plant components |
publisher |
Elsevier |
series |
Nuclear Engineering and Technology |
issn |
1738-5733 |
publishDate |
2018-12-01 |
description |
The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamic detection mechanism. Existing models and methods for fault diagnosis using different mathematical/statistical inferences lack incipient and novel faults detection capability. To this end, we propose a fault diagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) for component-level fault diagnosis. The technique integrates separately-built, separately-trained, specialized SVM modules capable of component-level fault diagnosis into a coherent intelligent system, with each SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginal faults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator and pressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a best-estimate thermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained with component level parameters that represent the steady state and selected faults in the components. For optimization purposes, we considered and compared the performances of different multiclass models in MATLAB, using different coding matrices, as well as different kernel functions on the representative data derived from the simulation of Qinshan I NPP. An optimum predictive model - the Error Correcting Output Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilized to diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluation methods are presented in this paper. Keywords: Fault diagnosis, Support vector machine, Error correcting output code, Reactor coolant system |
url |
http://www.sciencedirect.com/science/article/pii/S1738573317306988 |
work_keys_str_mv |
AT abiodunayodeji supportvectorensembleforincipientfaultdiagnosisinnuclearplantcomponents AT yongkuoliu supportvectorensembleforincipientfaultdiagnosisinnuclearplantcomponents |
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