A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring

doi:10.1016/j.cjche.2014.09.021

›› 2014, Vol. 22 ›› Issue (11/12): 1243-1253.DOI: 10.1016/j.cjche.2014.09.021

• 过程系统工程与过程安全 • 上一篇下一篇

A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring

Lianfang Cai, Xuemin Tian, Ni Zhang

College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China

收稿日期:2014-01-05 修回日期:2014-03-03 出版日期:2014-12-28 发布日期:2014-12-24
通讯作者: Xuemin Tian
基金资助:
Supported by the National Natural Science Foundation of China (61273160), the Natural Science Foundation of Shandong Province of China (ZR2011FM014), the Doctoral Fund of Shandong Province (BS2012ZZ011) and the Postgraduate Innovation Funds of China University of Petroleum (CX2013060).

A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring

Lianfang Cai, Xuemin Tian, Ni Zhang

College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China

Received:2014-01-05 Revised:2014-03-03 Online:2014-12-28 Published:2014-12-24
Supported by:
Supported by the National Natural Science Foundation of China (61273160), the Natural Science Foundation of Shandong Province of China (ZR2011FM014), the Doctoral Fund of Shandong Province (BS2012ZZ011) and the Postgraduate Innovation Funds of China University of Petroleum (CX2013060).

摘要/Abstract

摘要： Kernel independent component analysis (KICA) is a newly emerging nonlinear process monitoring method, which can extractmutually independent latent variables called independent components (ICs) fromprocess variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis (KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature. Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.

关键词: Process monitoring, Independent component analysis, Kernel trick, Time structure, Fault identification

Abstract: Kernel independent component analysis (KICA) is a newly emerging nonlinear process monitoring method, which can extractmutually independent latent variables called independent components (ICs) fromprocess variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis (KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature. Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.

Key words: Process monitoring, Independent component analysis, Kernel trick, Time structure, Fault identification

Lianfang Cai, Xuemin Tian, Ni Zhang. A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring[J]. , 2014, 22(11/12): 1243-1253.

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A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring

A Kernel Time Structure Independent Component Analysis Method for Nonlinear Process Monitoring

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