Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

Muddu Madakyaru, Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations


Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
Original languageEnglish (US)
Title of host publication2016 IEEE Symposium Series on Computational Intelligence (SSCI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781509042401
StatePublished - Feb 16 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.


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