文献类型：期刊文献

英文题名：Gaussian and non-Gaussian Double Subspace Statistical Process Monitoring Based on Principal Component Analysis and Independent Component Analysis

作者：Huang, Jian[1];Yan, Xuefeng[1]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2015

卷号：54

期号：3

起止页码：1015

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20150500479574);WOS:【SCI-EXPANDED(收录号:WOS:000348692700025)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China (2013CB733605), National Natural Science Foundation of China (21176073), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Bayesian networks - Fault detection - Numerical methods - Data handling - Gaussian noise (electronic) - Principal component analysis - Process monitoring - Statistical process control - Chemical analysis - Gaussian distribution - Inference engines

摘要：This study proposes a new statistical process monitoring method based on variable distribution characteristic (VDSPM) with consideration that variables submit to different distributions in chemical processes and that principal component analysis (PCA) and independent component analysis (ICA) are, respectively, suitable for processing data with Gaussian and non-Gaussian distribution. In VDSPM, D-test is first employed to identify the normality of process variables. The process variables under Gaussian distribution are classified into Gaussian subspace and the others belong to non-Gaussian subspace. PCA and ICA models are respectively built for fault detection in Gaussian and non-Gaussian subspaces. Bayesian inference is used to combine the monitoring results of the two subspaces to create a final statistic. The proposed method is applied to a numerical system and to the Tennessee Eastman benchmark process. Results proved that the proposed system outperformed the PCA and ICA methods.