Statistical process monitoring based on improved principal component analysis and its application to chemical processes  

作　　者：Chu-dong TONG Xue-feng YAN Yu-xin MA 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2013年第7期520-534,共15页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：supported by the National Basic Research Program of China (973 Program) (No. 2013CB733600);the National Natural Science Foundation of China (No. 21176073);the Program for New Century Excellent Talents in University (No. NCET-09-0346);the Fundamental Research Funds for the Central Universities, China

摘　　要：In this paper, a novel criterion is proposed to determine the retained principal components (PCs) that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs (CPCs). The new Hotelling's T2 statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process demonstrated the feasibility and effectiveness of the CPCs-based fault detection methods.In this paper, a novel criterion is proposed to determine the retained principal components （PCs） that capture the dominant variability of online monitored data. The variations of PCs were calculated according to their mean and covariance changes between the modeling sample and the online monitored data. The retained PCs containing dominant variations were selected and defined as correlative PCs （CPCs）. The new Hotelling＇s 7~ statistic based on CPCs was then employed to monitor the process. Case studies on the simulated continuous stirred tank reactor and the well-known Tennessee Eastman process dem- onstrated the feasibility and effectiveness of the CPCs-based fault detection methods.

关 键 词：Fault detection Principal component analysis (PCA) Correlative principal components (CPCs) Tennessee Eastman process 

分 类 号：TQ086.3[化学工程] TP277[自动化与计算机技术—检测技术与自动化装置]