Fault detection in a wastewater treatment plant

Imen Baklouti, Majdi Mansouri, Hazem Nounou, Mohamed Ben Slima, Ahmed Ben Hamida

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

7 Citations (Scopus)

Abstract

In this paper, Unscented Kalman filter (UKF) based Exponentially Weighted Moving Average (EWMA) is proposed for fault detection in a Wastewater Treatment Plant (WWTP). In the developed UKF-based EWMA, the UKF technique is used to compute the residual between the true and the estimated variable and the EWMA control chart is applied to detect the faults. The fault detection technique will be tested using simulated COST wastewater treatment ASM1 model. The detection results of the UKF-based EWMA technique are evaluated using three fault detection criteria: the false alarm rate (FAR), Average Run Length (ARL1) and the missed detection rate (MDR).

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017
EditorsAhmed Ben Hamida, Basel Solaiman, Ahmed Ben Slima, Mohammed El Hassouni, Mohammed Karim
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538605516
DOIs
Publication statusPublished - 19 Oct 2017
Externally publishedYes
Event3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017 - Fez, Morocco
Duration: 22 May 201724 May 2017

Publication series

NameProceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017

Conference

Conference3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017
Country/TerritoryMorocco
CityFez
Period22/05/1724/05/17

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