KPCA Plus FDA for Fault Detection

Authors:
Peiling Cui;Jiancheng Fang
Affiliations:
School of Instrumentation Science & Opto-electronics Engineering, Beijing University of Aeronautics & Astronautics, Beijing 100083, China;School of Instrumentation Science & Opto-electronics Engineering, Beijing University of Aeronautics & Astronautics, Beijing 100083, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

Citing 4
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Abstract

Kernel principal component analysis (KPCA) is widely used for fault detection. In this paper, a KPCA plus Fisher discriminant analysis (FDA) scheme is adopted to improve the fault detection performance of KPCA. Simulation results are given to show the effectiveness of the improvements for fault detection performance in terms of high fault detection rate.