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

Muddu Madakyaru; Fouzi Harrou; Ying Sun

doi:10.1109/SSCI.2016.7849880

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 proceeding › Conference contribution

7 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509042401
DOIs	https://doi.org/10.1109/SSCI.2016.7849880
Publication status	Published - 09-02-2017
Event	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 - Athens, Greece Duration: 06-12-2016 → 09-12-2016

Conference

Conference	2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Country/Territory	Greece
City	Athens
Period	06-12-16 → 09-12-16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems and Management
Control and Optimization
Artificial Intelligence

Access to Document

10.1109/SSCI.2016.7849880

Cite this

@inproceedings{c624808d14ed4b26a9b87da4555cf3e4,

title = "Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test",

abstract = "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.",

author = "Muddu Madakyaru and Fouzi Harrou and Ying Sun",

year = "2017",

month = feb,

day = "9",

doi = "10.1109/SSCI.2016.7849880",

language = "English",

booktitle = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 ; Conference date: 06-12-2016 Through 09-12-2016",

}

Madakyaru, M, Harrou, F & Sun, Y 2017, Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test. in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016., 7849880, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, Athens, Greece, 06-12-16. https://doi.org/10.1109/SSCI.2016.7849880

TY - GEN

T1 - Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test

AU - Madakyaru, Muddu

AU - Harrou, Fouzi

AU - Sun, Ying

PY - 2017/2/9

Y1 - 2017/2/9

N2 - 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.

AB - 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.

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U2 - 10.1109/SSCI.2016.7849880

DO - 10.1109/SSCI.2016.7849880

M3 - Conference contribution

AN - SCOPUS:85016086660

BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016

Y2 - 6 December 2016 through 9 December 2016

ER -

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

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