TY - GEN
T1 - Fault detection of an air quality monitoring network
AU - Baklouti, Raoudha
AU - Ben Hamida, Ahmed
AU - Mansouri, Majdi
AU - Nounou, Mohamed N.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - Concerns for the environment, health and safety are of major importance and have been attracting considerable attention around the globe due to the new environmental challenges that are threatening our planet. In this paper, we propose to enhance the fault detection of an air quality monitoring network (AQMN) by using wavelet principal component analysis (WPCA)-based on generalized likelihood ratio test (GLRT). The presence of measurement noise in the data and model uncertainties degrade the quality of fault detection (FD) techniques by increasing the rate of false alarms. Therefore, the objective of this paper is to enhance the FD of an AQMN by using wavelet representation of data, which is a powerful feature extraction tool to remove the noises from the data. Wavelet data representation has been used to enhance the FD abilities of principal component analysis. Therefore, in the current work, we propose to use WPCA-based on GLRT technique for FD. The fault detection performances of the WPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the detection efficiency of developed WPCA-based GLRT technique, when compared to classical PCA and WPCA techniques.
AB - Concerns for the environment, health and safety are of major importance and have been attracting considerable attention around the globe due to the new environmental challenges that are threatening our planet. In this paper, we propose to enhance the fault detection of an air quality monitoring network (AQMN) by using wavelet principal component analysis (WPCA)-based on generalized likelihood ratio test (GLRT). The presence of measurement noise in the data and model uncertainties degrade the quality of fault detection (FD) techniques by increasing the rate of false alarms. Therefore, the objective of this paper is to enhance the FD of an AQMN by using wavelet representation of data, which is a powerful feature extraction tool to remove the noises from the data. Wavelet data representation has been used to enhance the FD abilities of principal component analysis. Therefore, in the current work, we propose to use WPCA-based on GLRT technique for FD. The fault detection performances of the WPCA-based GLRT technique are shown using air quality monitoring network (AQMN). The results showed the detection efficiency of developed WPCA-based GLRT technique, when compared to classical PCA and WPCA techniques.
KW - Air Quality Monitoring Network
KW - Generalized Likelihood Ratio Test
KW - Wavelet Principle Component Analysis
UR - http://www.scopus.com/inward/record.url?scp=85024390816&partnerID=8YFLogxK
U2 - 10.1109/STA.2016.7952051
DO - 10.1109/STA.2016.7952051
M3 - Conference contribution
AN - SCOPUS:85024390816
T3 - 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings
SP - 229
EP - 233
BT - 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2016
Y2 - 19 December 2016 through 21 December 2016
ER -