TY - GEN
T1 - EWMA Kernel Generalized Likelihood Ratio Test for Fault Detection of Chemical Processes
AU - Baklouti, Raoudha
AU - Ben Hamida, Ahmed
AU - Mansouri, Majdi
AU - Nounou, Hazem
AU - Nounou, Mohamed
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.
AB - Fault Detection (FD) is a fundamental step in process monitoring. Owning to its simplicity and effectiveness to deal with nonlinear and highly correlated process variables, kernel principal component analysis (KPCA) has been successfully used in process monitoring. However, the major drawback of this method-based kernel generalized likelihood ratio test (KGLRT) is the neglect of small faults. Inspired by the effectiveness of this detection metric and motivated by the advantages of the univariate exponentially weighted movng average (EWMA), we propose, in this paper, a KPCA-based EWMA-KGLRT FD algorithm. Hence, its performance is illustrated and compared to the conventional KPCA-based KGLRT method through continuously simulated tank reactor (CSTR). In fact, the experimental results confirmed the performance of the proposed algorithm in terms of missed detection (MD) and false alarm (FA) rates.
KW - Continuously Stirred Tank Reactor (CSTR)
KW - Exponentially Weighted Moving Average (EWMA)
KW - Kernel generalized likelihood ratio (KGLRT)
KW - fault detection (FD)
KW - kernel principal component analysis (KPCA)
KW - monitoring
UR - http://www.scopus.com/inward/record.url?scp=85096555126&partnerID=8YFLogxK
U2 - 10.1109/ATSIP49331.2020.9231545
DO - 10.1109/ATSIP49331.2020.9231545
M3 - Conference contribution
AN - SCOPUS:85096555126
T3 - 2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
BT - 2020 International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2020
Y2 - 2 September 2020 through 5 September 2020
ER -