Adaptive multiblock kernel principal component analysis for monitoring complex industrial processes  

作　　者：Ying-wei ZHANG Yong-dong TENG 

机构地区：[1]MOE Key Lab of lntegrated Automation of Process Industry, Northeastern University, Shenyang 110004, China

出　　处：《Journal of Zhejiang University-Science C(Computers and Electronics)》2010年第12期948-955,共8页浙江大学学报C辑（计算机与电子（英文版）

基　　金：Project supported by the National Basic Research Program (973) of China (No. 2009CB320600) ;the National Natural Science Foun-dation of China (No. 60974057)

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.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 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]