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Journal of Zhejiang University SCIENCE C
ISSN 1869-1951(Print), 1869-196x(Online), Monthly
2010 Vol.11 No.12 P.948-955
Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
Abstract: Multiblock kernel principal component analysis (MBKPCA) has been proposed to isolate the faults and avoid the high computation cost. However, MBKPCA is not available for dynamic processes. To solve this problem, recursive MBKPCA is proposed for monitoring large scale processes. In this paper, we present a new recursive MBKPCA (RMBKPCA) algorithm, where the adaptive technique is adopted for dynamic characteristics. The proposed algorithm reduces the high computation cost, and is suitable for online model updating in the feature space. The proposed algorithm was applied to an industrial process for adaptive monitoring and found to efficiently capture the time-varying and nonlinear relationship in the process variables.
Key words: Recursive multiblock kernel principal component analysis (RMBPCA), Dynamic process, Nonlinear process
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DOI:
10.1631/jzus.C1000148
CLC number:
TP27
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On-line Access:
2010-12-09
Received:
2010-04-13
Revision Accepted:
2010-06-10
Crosschecked:
2010-06-29