지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.1
- 수록면
- 624 - 632 (9page)
이용수
초록· 키워드
오류제보하기
A self-diagnostic monitoring system is a system that has the ability to measure variousphysical quantities such as temperature, pressure, or acceleration from sensors scatteredover a mechanical system such as a power plant, in order to monitor its various states, andto make a decision about its health status. We have developed a self-diagnostic monitoringsystem for an air-operated valve system to be used in a nuclear power plant. In this study,we have tried to improve the self-diagnostic monitoring system to increase its reliability.
We have implemented three different machine learning algorithms, i.e., logistic regression,an artificial neural network, and a support vector machine. After each algorithm performsthe decision process independently, the decision-making module collects these individualdecisions and makes a final decision using a majority vote scheme. With this, we performedsome simulations and presented some of its results. The contribution of this studyis that, by employing more robust and stable algorithms, each of the algorithms performsthe recognition task more accurately. Moreover, by integrating these results and employingthe majority vote scheme, we can make a definite decision, which makes the selfdiagnosticmonitoring system more reliable.
목차
등록된 정보가 없습니다.
참고문헌 (14)
참고문헌 신청
A. Smola / 2010 / Introduction to Machine Learning / Cambridge University Press
C.M. Bishop / 2006 / Pattern Recognition and Machine Learning / Springer
G. Betta / 2000 / Instrument fault detection and isolation: state of the art and new research trends / IEEE Trans. Instrum. Meas 49 : 100 ~ 107
G. Park / 2006 / Piezoelectric active sensor self-diagnostics using electrical admittance measurements / J. Vib. Acoust 128 : 469 ~ 476
I. Steinwart / 2008 / Support Vector Machine / Springer
C.M. Bishop / 2006 / Pattern Recognition and Machine Learning / Springer
G. Betta / 2000 / Instrument fault detection and isolation: state of the art and new research trends / IEEE Trans. Instrum. Meas 49 : 100 ~ 107
G. Park / 2006 / Piezoelectric active sensor self-diagnostics using electrical admittance measurements / J. Vib. Acoust 128 : 469 ~ 476
I. Steinwart / 2008 / Support Vector Machine / Springer
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