Improved kernel principal component analysis for fault detection

Authors:
Peiling Cui;Junhong Li;Guizeng Wang
Affiliations:
Department of Automation, Tsinghua University, Beijing 100084, China and School of Instrumentation Science & Opto-electronics Engineering, Beijing University of Aeronautics & Astronautics, Beijing ...;Aigo Research Institute of Image Computing, Beijing 100089, China;Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This paper improves kernel principal component analysis (KPCA) for fault detection from two aspects. Firstly, a feature vector selection (FVS) scheme based on a geometrical consideration is given to reduce the computational complexity of KPCA when the number of samples becomes large. Secondly, 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 these improvements for fault detection performance in terms of low computational cost and high fault detection rate.