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

Content Provider	Springer Nature Link
Author	Wang, Bei Jiang, Qingchao Yan, Xuefeng
Copyright Year	2014
Abstract	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.
Starting Page	930
Ending Page	943
Page Count	14
File Format	PDF
ISSN	02561115
Journal	Korean Journal of Chemical Engineering
Volume Number	31
Issue Number	6
e-ISSN	19757220
Language	English
Publisher	Springer US
Publisher Date	2014-04-11
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Multi-block PCA Kullback-Leibler Divergence Bayesian Inference Plant-wide Process Monitoring Industrial Chemistry/Chemical Engineering Catalysis Materials Science Biotechnology
Content Type	Text
Resource Type	Article
Subject	Chemistry Chemical Engineering

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