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Fault Detection and Identification in NPP Instruments Using Kernel Principal Component Analysis

[+] Author Affiliations
Jianping Ma

University of Western Ontario, London, ON, Canada

Jin Jiang

University of Western Ontario, London, ON, Canada; Xi’an Jiaotong University, Xi’an, Shaanxi, China

Paper No. ICONE18-29777, pp. 765-771; 7 pages
  • 18th International Conference on Nuclear Engineering
  • 18th International Conference on Nuclear Engineering: Volume 1
  • Xi’an, China, May 17–21, 2010
  • Conference Sponsors: Nuclear Engineering Division
  • ISBN: 978-0-7918-4929-3
  • Copyright © 2010 by ASME


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.

Copyright © 2010 by ASME



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