Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper deve...

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Bibliographic Details
Published in:Transactions of the Institute of Measurement and Control 2020-04, Vol.42 (6), p.1225-1238
Main Authors: Bounoua, Wahiba, Benkara, Amina B, Kouadri, Abdelmalek, Bakdi, Azzeddine
Format: Article
Language:English
Subjects:
Algorithms
Chemical industry
Control charts
False alarms
Fault detection
Gaussian distribution
Monitoring
Multivariate analysis
Normal distribution
Principal components analysis
Statistical analysis
Subspaces
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Summary:Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution, KLD is computed on both principal and residual subspaces defined by PCA in a moving window to extract the local disparity information. The potentials of the proposed algorithm are afterwards demonstrated through an application on two well-known processes in chemical industries; the Tennessee Eastman process as a reference benchmark and three tank system as an experimental validation. The monitoring performance was compared to recent results from other multivariate statistical process monitoring (MSPM) techniques. The proposed method showed superior robustness and effectiveness recording the lowest average missed detection rate and false alarm rates in process fault detection.
ISSN:0142-3312
1477-0369
DOI:10.1177/0142331219888370