Fault detection of chemical processes using improved generalized likelihood ratio test

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

3 Citations (Scopus)

Abstract

In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. The FD problem will be addressed so that the kernel partial least square (KPLS) is used as a modeling framework and the generated residuals are evaluated using the developed EWMA-GLRT chart. The KPLS model is capable of dealing with high dimensional input-output nonlinear and multivariate data. Therefore, in this paper, KPLS-based EWMA-GLRT method will be utilized in practice to help improve FD of chemical processes. The FD performance is assessed and evaluated in terms of false alarm rate, missed detection rate and ARL1 values

Original languageEnglish
Title of host publication2017 22nd International Conference on Digital Signal Processing, DSP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2017-August
ISBN (Electronic)9781538618950
DOIs
Publication statusPublished - 3 Nov 2017
Event2017 22nd International Conference on Digital Signal Processing, DSP 2017 - London, United Kingdom
Duration: 23 Aug 201725 Aug 2017

Other

Other2017 22nd International Conference on Digital Signal Processing, DSP 2017
CountryUnited Kingdom
CityLondon
Period23/8/1725/8/17

Fingerprint

Fault detection
Statistics

ASJC Scopus subject areas

  • Signal Processing

Cite this

Mansouri, M., Nounou, H., Harkat, M-F., & Nounou, M. (2017). Fault detection of chemical processes using improved generalized likelihood ratio test. In 2017 22nd International Conference on Digital Signal Processing, DSP 2017 (Vol. 2017-August). [8096094] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDSP.2017.8096094

Fault detection of chemical processes using improved generalized likelihood ratio test. / Mansouri, Majdi; Nounou, Hazem; Harkat, Mohamed-Faouzi; Nounou, Mohamed.

2017 22nd International Conference on Digital Signal Processing, DSP 2017. Vol. 2017-August Institute of Electrical and Electronics Engineers Inc., 2017. 8096094.

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

Mansouri, M, Nounou, H, Harkat, M-F & Nounou, M 2017, Fault detection of chemical processes using improved generalized likelihood ratio test. in 2017 22nd International Conference on Digital Signal Processing, DSP 2017. vol. 2017-August, 8096094, Institute of Electrical and Electronics Engineers Inc., 2017 22nd International Conference on Digital Signal Processing, DSP 2017, London, United Kingdom, 23/8/17. https://doi.org/10.1109/ICDSP.2017.8096094
Mansouri M, Nounou H, Harkat M-F, Nounou M. Fault detection of chemical processes using improved generalized likelihood ratio test. In 2017 22nd International Conference on Digital Signal Processing, DSP 2017. Vol. 2017-August. Institute of Electrical and Electronics Engineers Inc. 2017. 8096094 https://doi.org/10.1109/ICDSP.2017.8096094
Mansouri, Majdi ; Nounou, Hazem ; Harkat, Mohamed-Faouzi ; Nounou, Mohamed. / Fault detection of chemical processes using improved generalized likelihood ratio test. 2017 22nd International Conference on Digital Signal Processing, DSP 2017. Vol. 2017-August Institute of Electrical and Electronics Engineers Inc., 2017.
@inproceedings{511369888294461589e38dd4d9b93bd4,
title = "Fault detection of chemical processes using improved generalized likelihood ratio test",
abstract = "In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. The FD problem will be addressed so that the kernel partial least square (KPLS) is used as a modeling framework and the generated residuals are evaluated using the developed EWMA-GLRT chart. The KPLS model is capable of dealing with high dimensional input-output nonlinear and multivariate data. Therefore, in this paper, KPLS-based EWMA-GLRT method will be utilized in practice to help improve FD of chemical processes. The FD performance is assessed and evaluated in terms of false alarm rate, missed detection rate and ARL1 values",
author = "Majdi Mansouri and Hazem Nounou and Mohamed-Faouzi Harkat and Mohamed Nounou",
year = "2017",
month = "11",
day = "3",
doi = "10.1109/ICDSP.2017.8096094",
language = "English",
volume = "2017-August",
booktitle = "2017 22nd International Conference on Digital Signal Processing, DSP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Fault detection of chemical processes using improved generalized likelihood ratio test

AU - Mansouri, Majdi

AU - Nounou, Hazem

AU - Harkat, Mohamed-Faouzi

AU - Nounou, Mohamed

PY - 2017/11/3

Y1 - 2017/11/3

N2 - In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. The FD problem will be addressed so that the kernel partial least square (KPLS) is used as a modeling framework and the generated residuals are evaluated using the developed EWMA-GLRT chart. The KPLS model is capable of dealing with high dimensional input-output nonlinear and multivariate data. Therefore, in this paper, KPLS-based EWMA-GLRT method will be utilized in practice to help improve FD of chemical processes. The FD performance is assessed and evaluated in terms of false alarm rate, missed detection rate and ARL1 values

AB - In this paper, we address the problem of fault detection (FD) of chemical processes using improved generalized likelihood ratio test. The improved GLRT is the method that combines the advantages of the exponentially weighted moving average (EWMA) filter with those of the GLRT method. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. The FD problem will be addressed so that the kernel partial least square (KPLS) is used as a modeling framework and the generated residuals are evaluated using the developed EWMA-GLRT chart. The KPLS model is capable of dealing with high dimensional input-output nonlinear and multivariate data. Therefore, in this paper, KPLS-based EWMA-GLRT method will be utilized in practice to help improve FD of chemical processes. The FD performance is assessed and evaluated in terms of false alarm rate, missed detection rate and ARL1 values

UR - http://www.scopus.com/inward/record.url?scp=85040362136&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85040362136&partnerID=8YFLogxK

U2 - 10.1109/ICDSP.2017.8096094

DO - 10.1109/ICDSP.2017.8096094

M3 - Conference contribution

VL - 2017-August

BT - 2017 22nd International Conference on Digital Signal Processing, DSP 2017

PB - Institute of Electrical and Electronics Engineers Inc.

ER -