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

作　　者：Zhengshun Fei Kangling Liu 

机构地区：[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

出　　处：《Chinese Journal of Chemical Engineering》2016年第6期775-786,共12页中国化学工程学报（英文版）

基　　金：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：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.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 de- fined 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 exem- plified 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 analysisWeightOnline process monitoringDynamic 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]