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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.1
- 수록면
- 1,306 - 1,313 (8page)
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초록· 키워드
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The randomness and incipient nature of certain faults in reactor systems warrant a robust and dynamicdetection 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 faultdiagnosis method that utilizes the flexibility of data-driven Support Vector Machine (SVM) forcomponent-level fault diagnosis. The technique integrates separately-built, separately-trained, specializedSVM modules capable of component-level fault diagnosis into a coherent intelligent system, witheach SVM module monitoring sub-units of the reactor coolant system. To evaluate the model, marginalfaults selected from the failure mode and effect analysis (FMEA) are simulated in the steam generator andpressure boundary of the Chinese CNP300 PWR (Qinshan I NPP) reactor coolant system, using a bestestimatethermal-hydraulic code, RELAP5/SCDAP Mod4.0. Multiclass SVM model is trained withcomponent level parameters that represent the steady state and selected faults in the components. Foroptimization purposes, we considered and compared the performances of different multiclass models inMATLAB, using different coding matrices, as well as different kernel functions on the representative dataderived from the simulation of Qinshan I NPP. An optimum predictive model - the Error CorrectingOutput Code (ECOC) with TenaryComplete coding matrix - was obtained from experiments, and utilizedto diagnose the incipient faults. Some of the important diagnostic results and heuristic model evaluationmethods are presented in this paper
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참고문헌 (25)
참고문헌 신청
C. Price / 1997 / Multiple fault diagnosis from FMEA / IAAI-97 Proceedings
E.R. McDermott / 1996 / The Basics of FMEA / Productivity, Inc
Abiodun Ayodeji / 2018 / Knowledge base operator support system for nuclear power plant fault diagnosis / Progress in Nuclear Energy 105:42~50
M. Alamaniotis / 2011 / Online surveillance of nuclear power plant peripheral components using support vector regression / Proceedings of the International Symposium on Future I&C for Nuclear Power Plants, Cognitive Systems Engineering on Process Control, and International Symposium on Symbiotic Nuclear Power Systems :1230
A. Ayodeji / 2018 / SVR optimization with soft computing algorithms for incipient SGTR diagnosis / Ann. Nucl. Energy 121:89~100
E.R. McDermott / 1996 / The Basics of FMEA / Productivity, Inc
Abiodun Ayodeji / 2018 / Knowledge base operator support system for nuclear power plant fault diagnosis / Progress in Nuclear Energy 105:42~50
M. Alamaniotis / 2011 / Online surveillance of nuclear power plant peripheral components using support vector regression / Proceedings of the International Symposium on Future I&C for Nuclear Power Plants, Cognitive Systems Engineering on Process Control, and International Symposium on Symbiotic Nuclear Power Systems :1230
A. Ayodeji / 2018 / SVR optimization with soft computing algorithms for incipient SGTR diagnosis / Ann. Nucl. Energy 121:89~100
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