Multivariate Statistical Process Monitoring Using Robust Nonlinear Principal Component Analysis  

作　　者：赵仕健 徐用懋 

机构地区：[1]Department of Automation, Tsinghua University, Beijing 100084, China

出　　处：《Tsinghua Science and Technology》2005年第5期582-586,共5页清华大学学报（自然科学版（英文版）

基　　金：Supported by the National High-Tech Research and Development (863) Program of China (No. 2001AA413320)

摘　　要：The principal component analysis （PCA） algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the nonlinear PCA （NLPCA） using autoassociative bottle-neck neural networks is so sensitive that the obtained model differs significantly from the underlying system. In this paper, a robust version of NLPCA is introduced by replacing the generally used error criterion mean squared error with a mean log squared error. This is followed by a concise analysis of the corresponding training method. A novel multivariate statistical process monitoring （MSPM） scheme incorporating the proposed robust NLPCA technique is then investigated and its efficiency is assessed through application to an industrial fluidized catalytic cracking plant. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms and is, hence, expected to better monitor real-world processes.The principal component analysis （PCA） algorithm is widely applied in a diverse range of fields for performance assessment, fault detection, and diagnosis. However, in the presence of noise and gross errors, the nonlinear PCA （NLPCA） using autoassociative bottle-neck neural networks is so sensitive that the obtained model differs significantly from the underlying system. In this paper, a robust version of NLPCA is introduced by replacing the generally used error criterion mean squared error with a mean log squared error. This is followed by a concise analysis of the corresponding training method. A novel multivariate statistical process monitoring （MSPM） scheme incorporating the proposed robust NLPCA technique is then investigated and its efficiency is assessed through application to an industrial fluidized catalytic cracking plant. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms and is, hence, expected to better monitor real-world processes.

关 键 词：robust nonlinear principal component analysis autoassociative networks multivariate statisticaprocess monitoring （MSPM） fluidized catalytic cracking unit （FCCU） 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]