文献类型：期刊文献

英文题名：Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference

作者：Jiang, Qingchao[1,2];Yan, Xuefeng[1];Huang, Biao[2]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada

年份：2016

卷号：63

期号：1

起止页码：377

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

收录：;EI(收录号：20161202134006);WOS:【SCI-EXPANDED(收录号:WOS:000366933100035)】；

基金：This work was supported in part by the 973 Project of China under Grant 2013CB733600, in part by the National Natural Science Foundation of China under Grant 21176073, in part by the Fundamental Research Funds for the Central Universities, in part by the Taishan Visiting Scholar Program of Shandong Province, and in part by Alberta Innovates Technology Futures.

语种：英文

外文关键词：Bayesian inference; distributed monitoring; fault-relevant variable selection; principal component analysis (PCA)

摘要：Multivariate statistical process monitoring involves dimension reduction and latent feature extraction in large-scale processes and typically incorporates all measured variables. However, involving variables without beneficial information may degrade monitoring performance. This study analyzes the effect of variable selection on principal component analysis (PCA) monitoring performance. Then, it proposes a fault-relevant variable selection and Bayesian inference-based distributed method for efficient fault detection and isolation. First, the optimal subset of variables is identified for each fault using an optimization algorithm. Second, a sub-PCA model is established in each subset. Finally, the monitoring results of all of the subsets are combined through Bayesian inference. The proposed method reduces redundancy and complexity, explores numerous local behaviors, and provides accurate description of faults, thus improving monitoring performance significantly. Case studies on a numerical example, the Tennessee Eastman benchmark process, and an industrial-scale plant demonstrate the efficiency.