FAULT DETECTION AND IDENTIFICATION IN NPP INSTRUMENTS USING KERNEL PRINCIPAL COMPONENT ANALYSIS

摘要：

In this paper, kernel principal component analysis (KPCA) is studied for fault detection and identification in the instruments of nuclear power plants. We propose to use mean values of the sensor reconstruction errors of a KPCA model for fault isolation and identification. They provide useful information about the directions and magnitudes of detected faults, which are usually not available from other fault isolation techniques. The performance of the method is demonstrated by applications to real NPP measurements.

作者: Jianping Ma Jin Jiang

作者单位: Department of Electrical and Computer Engineering University of Western Ontario London, Ontario, N6A Department of Electrical and Computer Engineering University of Western Ontario London, Ontario, N6A

会议类型: 国际会议

会议名称: 18th International Conference on Nuclear Engineering(第18届国际核能工程大会 ICONE 18)

会议地点: 西安

会议语种:英文

页码: 1-7

在线出版日期: 2010-05-17（万方平台首次上网日期，不代表论文的发表时间）