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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.12 No.4
- 발행연도
- 2017.7
- 수록면
- 1,406 - 1,416 (11page)
이용수
초록· 키워드
In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.
#Auto-associative kernel regression
#Bagging method
#Fault detection and identification
#Thermal power plant
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목차
- Abstract
- 1. Introduction
- 2. Bagged Auto-associative Kernel Regression
- 3. Bagged Auto-associative Kernel Regression
- 4. Overview of the Proposed Fault Detection and Identification Approach
- 5. Description of the Target System: A Coal-fired Thermal Power Plant
- 6. Experimental Results
- 7. Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2018-560-000908589
