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Improved nonlinear fault detection technique and statistical analysis

Authors

  • Yingwei Zhang,

    Corresponding author
    1. Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, Liaoning 110004, P. R. China
    • Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, Liaoning 110004, P. R. China
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  • S. Joe Qin

    Corresponding author
    1. The Mork Family, Dept. of Chemical Engineering and Materials Science, Ming Hsieh Department of Electrical Engineering, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, 925 Bloom Walk, HED 210, Los Angeles, CA 90089
    • The Mork Family, Dept. of Chemical Engineering and Materials Science, Ming Hsieh Department of Electrical Engineering, Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, 925 Bloom Walk, HED 210, Los Angeles, CA 90089
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Abstract

In this article, first, some drawbacks of original Kernel Principal Component Analysis (KPCA) and Kernel Independent Component Analysis (KICA) are analyzed. Then the KPCA and KICA for multivariate statistical process monitoring (MSPM) are improved. The drawbacks of original KPCA and KICA are as follows: The data mapped into feature space become redundant; linear data introduce errors while the kernel trick is used; computation time increases with the number of samples. To solve the above problems, the original KPCA and KICA for MSPM are improved: similarity factors of the observed data in the input and feature space are defined; similar characteristics are measured; similar data are removed according to the similarity measurements; and k-means clustering in feature space is used to isolate different classes. Specifically, the similarity concept of data in one group is first proposed. Applications of the proposed approach indicate that improved KPCA and KICA effectively capture the nonlinearities. © 2008 American Institute of Chemical Engineers AIChE J, 2008

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