Online process monitoring for complex systems with dynamic weighted principal component analysis

doi:10.1016/j.cjche.2016.05.038

Chinese Journal of Chemical Engineering ›› 2016, Vol. 24 ›› Issue (6): 775-786.DOI: 10.1016/j.cjche.2016.05.038

• 第25届中国过程控制会议专栏 • 上一篇下一篇

Online process monitoring for complex systems with dynamic weighted principal component analysis

Zhengshun Fei¹, Kangling Liu²

1 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China;
2 State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

收稿日期:2015-10-31 修回日期:2016-02-18 出版日期:2016-06-28 发布日期:2016-07-12
通讯作者: Zhengshun Fei
基金资助:
Supported by the National Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016), the Natural Science Foundation of Zhejiang Province (LQ15F030006), and the Science and Technology Program Project of Zhejiang Province (2015C33033).

Online process monitoring for complex systems with dynamic weighted principal component analysis

Zhengshun Fei¹, Kangling Liu²

1 School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China;
2 State Key Lab of Industrial Control Technology, Institute of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received:2015-10-31 Revised:2016-02-18 Online:2016-06-28 Published:2016-07-12
Contact: Zhengshun Fei
Supported by:
Supported by the National Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016), the Natural Science Foundation of Zhejiang Province (LQ15F030006), and the Science and Technology Program Project of Zhejiang Province (2015C33033).

摘要/Abstract

摘要： Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach. The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

关键词: Principal component analysis, Weight, Online process monitoring, Dynamic

Abstract: Conventional multivariate statistical methods for process monitoring may not be suitable for dynamic processes since they usually rely on assumptions such as time invariance or uncorrelation. We are therefore motivated to propose a new monitoring method by compensating the principal component analysis with a weight approach. The proposed monitor consists of two tiers. The first tier uses the principal component analysis method to extract cross-correlation structure among process data, expressed by independent components. The second tier estimates auto-correlation structure among the extracted components as auto-regressive models. It is therefore named a dynamic weighted principal component analysis with hybrid correlation structure. The essential of the proposed method is to incorporate a weight approach into principal component analysis to construct two new subspaces, namely the important component subspace and the residual subspace, and two new statistics are defined to monitor them respectively. Through computing the weight values upon a new observation, the proposed method increases the weights along directions of components that have large estimation errors while reduces the influences of other directions. The rationale behind comes from the observations that the fault information is associated with online estimation errors of auto-regressive models. The proposed monitoring method is exemplified by the Tennessee Eastman process. The monitoring results show that the proposed method outperforms conventional principal component analysis, dynamic principal component analysis and dynamic latent variable.

Key words: Principal component analysis, Weight, Online process monitoring, Dynamic

Zhengshun Fei, Kangling Liu. Online process monitoring for complex systems with dynamic weighted principal component analysis[J]. Chinese Journal of Chemical Engineering, 2016, 24(6): 775-786.

Zhengshun Fei, Kangling Liu. Online process monitoring for complex systems with dynamic weighted principal component analysis[J]. Chin.J.Chem.Eng., 2016, 24(6): 775-786.

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Online process monitoring for complex systems with dynamic weighted principal component analysis

Online process monitoring for complex systems with dynamic weighted principal component analysis

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