Fault detection of chemical processes using improved generalized likelihood ratio test

Majdi Mansouri, Hazem Nounou, Mohamed Faouzi Harkat, Mohamed Nounou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 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.

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

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2017-August

Conference

Conference2017 22nd International Conference on Digital Signal Processing, DSP 2017
Country/TerritoryUnited Kingdom
CityLondon
Period23/08/1725/08/17

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