Enhanced operation of wastewater treatment plant using state estimation-based fault detection strategies

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

Research output: Contribution to journalArticle

Abstract

Fault detection is essential for monitoring of various biological processes and becomes even more important in that context. This paper, therefore, presents an enhanced tool that merges state estimation with fault detection (FD) methods to improve monitoring of biological processes. The proposed technique, so-called particle filter (PF)-based maximum double adaptive exponential weighted moving average (EWMA) chart, involves two steps. First, the states of the biological processes are estimated using the PF method. In the second step, the faults are detected using the maximum double adaptive EWMA chart. The proposed method is based on the maximum of the absolute values of the EWMA statistics, one monitoring adaptively the variance and the other controlling the mean. The FD performance is studied utilising a wastewater treatment model. The detection performances are assessed in terms of missed detection rate, false alarm rate, detection speed, sensibility to fault sizes and robustness to noise levels.

Original languageEnglish
JournalInternational Journal of Control
DOIs
Publication statusPublished - 1 Jan 2019

Fingerprint

State estimation
Fault detection
Wastewater treatment
Monitoring
Statistics

Keywords

  • exponential weighted moving average
  • fault detection
  • Particle filter
  • state estimation
  • wastewater treatment plant

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

Cite this

@article{d9e22af5707940a0ae8e37b9030f126c,
title = "Enhanced operation of wastewater treatment plant using state estimation-based fault detection strategies",
abstract = "Fault detection is essential for monitoring of various biological processes and becomes even more important in that context. This paper, therefore, presents an enhanced tool that merges state estimation with fault detection (FD) methods to improve monitoring of biological processes. The proposed technique, so-called particle filter (PF)-based maximum double adaptive exponential weighted moving average (EWMA) chart, involves two steps. First, the states of the biological processes are estimated using the PF method. In the second step, the faults are detected using the maximum double adaptive EWMA chart. The proposed method is based on the maximum of the absolute values of the EWMA statistics, one monitoring adaptively the variance and the other controlling the mean. The FD performance is studied utilising a wastewater treatment model. The detection performances are assessed in terms of missed detection rate, false alarm rate, detection speed, sensibility to fault sizes and robustness to noise levels.",
keywords = "exponential weighted moving average, fault detection, Particle filter, state estimation, wastewater treatment plant",
author = "Imen Baklouti and Majdi Mansouri and Hamida, {Ahmed Ben} and Hazem Nounou and Mohamed Nounou",
year = "2019",
month = "1",
day = "1",
doi = "10.1080/00207179.2019.1590735",
language = "English",
journal = "International Journal of Control",
issn = "0020-7179",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - Enhanced operation of wastewater treatment plant using state estimation-based fault detection strategies

AU - Baklouti, Imen

AU - Mansouri, Majdi

AU - Hamida, Ahmed Ben

AU - Nounou, Hazem

AU - Nounou, Mohamed

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Fault detection is essential for monitoring of various biological processes and becomes even more important in that context. This paper, therefore, presents an enhanced tool that merges state estimation with fault detection (FD) methods to improve monitoring of biological processes. The proposed technique, so-called particle filter (PF)-based maximum double adaptive exponential weighted moving average (EWMA) chart, involves two steps. First, the states of the biological processes are estimated using the PF method. In the second step, the faults are detected using the maximum double adaptive EWMA chart. The proposed method is based on the maximum of the absolute values of the EWMA statistics, one monitoring adaptively the variance and the other controlling the mean. The FD performance is studied utilising a wastewater treatment model. The detection performances are assessed in terms of missed detection rate, false alarm rate, detection speed, sensibility to fault sizes and robustness to noise levels.

AB - Fault detection is essential for monitoring of various biological processes and becomes even more important in that context. This paper, therefore, presents an enhanced tool that merges state estimation with fault detection (FD) methods to improve monitoring of biological processes. The proposed technique, so-called particle filter (PF)-based maximum double adaptive exponential weighted moving average (EWMA) chart, involves two steps. First, the states of the biological processes are estimated using the PF method. In the second step, the faults are detected using the maximum double adaptive EWMA chart. The proposed method is based on the maximum of the absolute values of the EWMA statistics, one monitoring adaptively the variance and the other controlling the mean. The FD performance is studied utilising a wastewater treatment model. The detection performances are assessed in terms of missed detection rate, false alarm rate, detection speed, sensibility to fault sizes and robustness to noise levels.

KW - exponential weighted moving average

KW - fault detection

KW - Particle filter

KW - state estimation

KW - wastewater treatment plant

UR - http://www.scopus.com/inward/record.url?scp=85063582517&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85063582517&partnerID=8YFLogxK

U2 - 10.1080/00207179.2019.1590735

DO - 10.1080/00207179.2019.1590735

M3 - Article

JO - International Journal of Control

JF - International Journal of Control

SN - 0020-7179

ER -