TY - GEN
T1 - Novel Fault Detection Approach of Biological Wastewater Treatment Plants
AU - Baklouti, Imen
AU - Mansouri, Majdi
AU - Ben Hamida, Ahmed
AU - Nounou, Hazem
AU - Nounou, Mohamed
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - It is well known that Exponentially Weighted Moving Average (EWMA) chart is designed to be optimal and efficient to quickly detect small faults. However, the classical EWMA can not perform well in the case of simultaneously large and small faults. To address this limitation, we propose to use an adaptive or a variable parameters control chart. Therefore, in this paper, we propose a novel approach, called particle filter (PF)-based adaptive EWMA (AEWMA) chart, with time-varying smoothing parameter lambda, to detect the fault in Wastewater Treatment Plant (WWTP) process. So that, the PF is applied to compute the residuals, and the AEWMA chart is used to detect the faults. The validation of the developed PF-based AEWMA technique is done using a simulated benchmark COST WWTP BSM1 model. The proposed PF-based AEWMA approach showed better detection abilities when compared to the classical EWMA and Shewhart charts.
AB - It is well known that Exponentially Weighted Moving Average (EWMA) chart is designed to be optimal and efficient to quickly detect small faults. However, the classical EWMA can not perform well in the case of simultaneously large and small faults. To address this limitation, we propose to use an adaptive or a variable parameters control chart. Therefore, in this paper, we propose a novel approach, called particle filter (PF)-based adaptive EWMA (AEWMA) chart, with time-varying smoothing parameter lambda, to detect the fault in Wastewater Treatment Plant (WWTP) process. So that, the PF is applied to compute the residuals, and the AEWMA chart is used to detect the faults. The validation of the developed PF-based AEWMA technique is done using a simulated benchmark COST WWTP BSM1 model. The proposed PF-based AEWMA approach showed better detection abilities when compared to the classical EWMA and Shewhart charts.
KW - Adaptive Exponentially Weighted Moving Average (AEWMA)
KW - Fault Detection (FD)
KW - Particle Filter (PF)
KW - Wastewater Treatment Plant (WWTP)
UR - https://www.scopus.com/pages/publications/85062214350
U2 - 10.1109/SMC.2018.00456
DO - 10.1109/SMC.2018.00456
M3 - Conference contribution
AN - SCOPUS:85062214350
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 2669
EP - 2674
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -