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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (12): 948-955    DOI: 10.1631/jzus.C1000148
    
Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
Ying-wei Zhang, Yong-dong Teng
MOE Key Lab of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China
Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes
Ying-wei Zhang, Yong-dong Teng
MOE Key Lab of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China
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摘要： 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.
关键词： Recursive multiblock kernel principal component analysis (RMBPCA);  Dynamic process;  Nonlinear process    
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
收稿日期: 2010-04-13 出版日期: 2010-12-09
CLC:  TP27  
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Ying-wei Zhang, Yong-dong Teng. Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes. Front. Inform. Technol. Electron. Eng., 2010, 11(12): 948-955.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1000148    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I12/948

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