文献类型：期刊文献

英文题名：Multiblock Independent Component Analysis Integrated with Hellinger Distance and Bayesian Inference for Non-Gaussian Plant-Wide Process Monitoring

作者：Jiang, Qingchao[1];Wang, Bei[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：54

期号：9

起止页码：2497

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20151100639475);WOS:【SCI-EXPANDED(收录号:WOS:000351186900010)】；

基金：The authors gratefully acknowledge the support of 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 - Inference engines - Process control - Process monitoring - Probability distributions

摘要：A novel multiblock plant-wide process monitoring method based on Hellinger distance (HD), Bayesian inference, and independent component analysis (ICA) (HDBICA) is proposed in this paper. Multiblock methods are usually employed for plant-wide process monitoring; however, block division is usually based on prior process knowledge that may not always be available. This paper proposes a totally data-driven multiblock monitoring method that employs HD to divide blocks automatically. Variables with similar probability distributions are divided into the same block on the basis of HD, and sub-ICA models are built for sub-block status monitoring. Finally, the monitoring results from all blocks are combined on the basis of Bayesian inference. HDBICA is exemplified by using a numerical study and the Tennessee-Eastman benchmark process. The monitoring results indicate that the performance of HDBICA is superior to the performances of ICA, kernel ICA, and other state-of-the-art variant-based methods.