Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
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Improved Anomaly Detection Using Multi-scale PLS.pdf
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Conference PaperKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Statistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2017-02-16Online Publication Date
2017-02-16Print Publication Date
2016-12Permanent link to this record
http://hdl.handle.net/10754/623850
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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.Citation
Madakyaru M, Harrou F, Sun Y (2016) Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.doi.org/10.1109/SSCI.2016.7849880.Sponsors
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.Conference/Event name
2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016Additional Links
http://ieeexplore.ieee.org/document/7849880/ae974a485f413a2113503eed53cd6c53
10.1109/SSCI.2016.7849880