TY - GEN
T1 - Recursive kernel PCA-based GLRT for fault detection
T2 - 2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017
AU - Baklouti, Raoudha
AU - Mansouri, Majdi
AU - Nounou, Hazem
AU - Nounou, Mohamed
AU - Hamida, Ahmed Ben
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/18
Y1 - 2017/10/18
N2 - This paper aims to improve the use of generalized likelihood ratio test (GLRT) method for fault detection. To achieve this objective, nonlinear fault detection method will be developed. Kernel principal component analysis (kPCA) models have been widely used to represent nonlinear systems. KPCA models rely of transforming the data in a linear form to a higher dimensional spacee. Unfortunately, kPCA models are batch, i.e., they require the availability of the process data before constructing the model. In most situations, however, fault detection is needed online, i.e., as the data are collected from the process. Therefore, recursive kPCA fault detection technique will be developed in order to extend the advantages of the GLRT to online processes. The fault detection performances of the recursive kPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the effectiveness of the developed algorithm over conventional method.
AB - This paper aims to improve the use of generalized likelihood ratio test (GLRT) method for fault detection. To achieve this objective, nonlinear fault detection method will be developed. Kernel principal component analysis (kPCA) models have been widely used to represent nonlinear systems. KPCA models rely of transforming the data in a linear form to a higher dimensional spacee. Unfortunately, kPCA models are batch, i.e., they require the availability of the process data before constructing the model. In most situations, however, fault detection is needed online, i.e., as the data are collected from the process. Therefore, recursive kPCA fault detection technique will be developed in order to extend the advantages of the GLRT to online processes. The fault detection performances of the recursive kPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the effectiveness of the developed algorithm over conventional method.
KW - Air Quality Monitoring Network
KW - GLRT
KW - Recursive kernel PCA
UR - http://www.scopus.com/inward/record.url?scp=85040022764&partnerID=8YFLogxK
U2 - 10.1109/SM2C.2017.8071839
DO - 10.1109/SM2C.2017.8071839
M3 - Conference contribution
AN - SCOPUS:85040022764
T3 - 2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017
SP - 152
EP - 155
BT - 2017 International Conference on Smart, Monitored and Controlled Cities, SM2C 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 February 2017 through 19 February 2017
ER -