@inproceedings{b060dc65a3354ac9bfc6cf68240fcd15,
title = "Fault detection of chemical processes using KPCA-based GLRT technique",
abstract = "In this paper, we address the problem of nonlinear fault detection of chemical processes. The objective is to extend our previous work [1] to provide a better performance in terms of fault detection accuracies by developing a pre-image kernel PCA (KPCA)-based Generalized Likelihood Ratio Test (GLRT) technique. The benefit of the pre-image kPCA technique lies in its ability to compute the residual in the original space using the KPCA from the feature space. In addition, GLRT provides more accurate results in terms of fault detection. The performance of the developed pre-image KPCA-based GLRT fault detection technique is evaluated using simulated continuously stirred tank reactor (CSTR) model.",
keywords = "CSTR process, Fault Detection, Generalized Likelihood Ratio Test, Pre-image Kernel, Principal Component Analysis",
author = "Raoudha Baklouti and Majdi Mansouri and Hazem Nounou and Mohamed Nounou and {Ben Slima}, Mohamed and {Ben Hamida}, Ahmed",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017 ; Conference date: 22-05-2017 Through 24-05-2017",
year = "2017",
month = oct,
day = "19",
doi = "10.1109/ATSIP.2017.8075513",
language = "English",
series = "Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Hamida, {Ahmed Ben} and Basel Solaiman and Slima, {Ahmed Ben} and {El Hassouni}, Mohammed and Mohammed Karim",
booktitle = "Proceedings - 3rd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2017",
address = "United States",
}