文献类型：期刊文献

英文题名：Fault detection and identification using a Kullback-Leibler divergence based multi-block principal component analysis and bayesian inference

作者：Wang, Bei[1];Jiang, Qingchao[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：2014

卷号：31

期号：6

起止页码：930

外文期刊名：KOREAN JOURNAL OF CHEMICAL ENGINEERING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000337060500003)】；

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：Multi-block PCA; Kullback-Leibler Divergence; Bayesian Inference; Plant-wide Process Monitoring

摘要：Considering the huge number of variables in plant-wide process monitoring and complex relationships (linear, nonlinear, partial correlation, or independence) among these variables, multivariate statistical process monitoring (MSPM) performance may be deteriorated especially by the independent variables. Meanwhile, whether related variables keep high concordance during the variation process is still a question. Under this circumstance, a multi-block technology based on mathematical statistics method, Kullback-Leibler Divergence, is proposed to put the variables having similar statistical characteristics into the same block, and then build principal component analysis (PCA) models in each low-dimensional subspace. Bayesian inference is also employed to combine the monitoring results from each sub-block into the final monitoring statistics. Additionally, a novel fault diagnosis approach is developed for fault identification. The superiority of the proposed method is demonstrated by applications on a simple simulated multivariate process and the Tennessee Eastman benchmark process.