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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.1
- 수록면
- 176 - 186 (11page)
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초록· 키워드
오류제보하기
Pattern classifications have become important tools for fault diagnosis in nuclear powerplants (NPP). However, it is often difficult to obtain training data under fault conditionsto train a supervised classification model. By contrast, normal plant operating data canbe easily made available through increased deployment of supervisory, control, and dataacquisition systems. Such data can also be used to train classification models to improvethe performance of fault diagnosis scheme.
In this paper, a fault diagnosis scheme based on semisupervised classification (SSC)scheme is developed. In this scheme, new measurements collected from the plant areintegrated with data observed under fault conditions to train the SSC models. The trainedmodels are subsequently applied to new measurements for fault diagnosis. In comparisonwith supervised classifiers, the proposed scheme requires significantly fewer data collectedunder fault conditions to train the classifier.
The developed scheme has been validated using different fault scenarios on a desktopNPP simulator as well as on a physical NPP simulator using a graph-based SSC algorithm.
All the considered faults have been successfully diagnosed.
The results have demonstrated that SSC is a promising tool for fault diagnosis in NPPs
목차
등록된 정보가 없습니다.
참고문헌 (30)
참고문헌 신청
A. R. Webb / 2011 / Statistical Pattern Recognition / Wiley
A. Singh / 2008 / Unlabeled data: now it helps, now it doesn't / Adv. Neural Inf. Process. Syst 21 : 1513 ~ 1520
A. Widodo / 2007 / Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors / Expert Syst. Appl 33 : 241 ~ 250
A. Widodo / 2007 / Support vector machine in machine condition monitoring and fault diagnosis / Mech. Syst. Signal Process 21 : 2560 ~ 2574
A.Y. Ng / 2001 / On spectral clustering:analysis and an algorithm / Adv. Neural Inf. Process. Syst 14 : 849 ~ 856
A. Singh / 2008 / Unlabeled data: now it helps, now it doesn't / Adv. Neural Inf. Process. Syst 21 : 1513 ~ 1520
A. Widodo / 2007 / Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors / Expert Syst. Appl 33 : 241 ~ 250
A. Widodo / 2007 / Support vector machine in machine condition monitoring and fault diagnosis / Mech. Syst. Signal Process 21 : 2560 ~ 2574
A.Y. Ng / 2001 / On spectral clustering:analysis and an algorithm / Adv. Neural Inf. Process. Syst 14 : 849 ~ 856
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