Enhancement of Incipient Fault Detection and Estimation using the Multivariate Kullback-Leibler Divergence

Abstract : Fault detection and diagnosis methods have to deal with large variable data sets encountered in complex industrial systems. Solutions to this problem require multivariate statistics approaches often focused on the reduction of the space dimension. In this paper we propose a fault detection and estimation approach using Multivariate Kullback-Leibler Divergence (MKLD) to cope with the negative effects due dimension reduction while using Principal Component Analysis (PCA). The obtained results show its superiority on the usual PCA-KLD based approach. An analytical model of the MKLD is proposed and validated for low severity fault (incipient fault) detection and estimation in noisy environment operating conditions.
Type de document :
Communication dans un congrès
IEEE. 24th European Signal Processing Conference (EUSIPCO), Aug 2016, Budapest, Hungary. pp.543-547, 〈10.1109/eusipco.2016.7760480 〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01366659
Contributeur : Claude Delpha <>
Soumis le : jeudi 15 septembre 2016 - 10:14:55
Dernière modification le : jeudi 26 avril 2018 - 16:32:17

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Abdulrahman Youssef, Claude Delpha, Demba Diallo. Enhancement of Incipient Fault Detection and Estimation using the Multivariate Kullback-Leibler Divergence. IEEE. 24th European Signal Processing Conference (EUSIPCO), Aug 2016, Budapest, Hungary. pp.543-547, 〈10.1109/eusipco.2016.7760480 〉. 〈hal-01366659〉

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