文献类型：期刊文献

英文题名：Dynamic process fault detection and diagnosis based on dynamic principal component analysis, dynamic independent component analysis and Bayesian inference

作者：Huang, Jian[1];Yan, Xuefeng[1]

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

年份：2015

卷号：148

起止页码：115

外文期刊名：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

收录：;EI(收录号：20242616493136);WOS:【SCI-EXPANDED(收录号:WOS:000364885900012)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China (2013CB733600), National Natural Science Foundation of China (21176073), Program for New Century Excellent Talents in University (NCET-09-0346) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Process monitoring; J-B test; Dynamic principal component analysis; Dynamic independent component analysis; Bayesian Inference

摘要：Dynamic principal component analysis (DPCA) and dynamic independent component analysis (DICA), as the frequently-used dimensional reduction methods, have been widely applied to monitor dynamic process. Considering the respective advantages of DPCA and DICA in different data distribution characteristics, this paper proposes a novel process monitoring algorithm named DPCA, DICA and Bayesian Inference (DPCA-DICA-BI). The main idea of DPCA-DICA-BI is to put the process variables with same distribution characteristic (Gaussian or non-Gaussian) into a block on the basis of the variable normality by Jarque-Bera test and then to respectively apply DPCA and DICA in Gaussian and non-Gaussian blocks. Finally, to combine the monitoring performance of both blocks, Bayesian inference is employed to make an integrated decision. The DPCA-DICA-BI as well as PCA, ICA, DPCA, and DICA has been used to a numerical example and Tennessee Eastman process. The simulation results show the superiority of DPCA-DICA-BI. (C) 2015 Elsevier B.V. All rights reserved.