Mohamed-Faouzi HARKAT received his Eng. degree in automation from Annaba University, Algeria in 1996, his Ph.D. degree from Institut National Polytechnique de Lorraine (INPL), France in 2003 and his Algerian "Accreditation to supervise researches" (HDR), from Annaba University, Algeria in 2006. He is now Professor in the Department of Electronic at Annaba University, Algeria.
His research interests include fault diagnosis, process modelling and monitoring, multivariate statistical approaches and neural networks.
Abstract: In this paper a sensor fault detection and isolation procedure based on principal component analysis is proposed to monitor an air quality monitoring network. The PCA model of the network is optimal with respect to a reconstruction error criterion. The sensor fault detection is carried out in various residual subspaces using a new detection index. For our application, this index improves the performance compared to classical detection index SPE. The reconstruction approach allows, on one hand, to isolate the faulty sensors and, on the other hand, to estimate the fault amplitudes.
Abstract: In this paper a new algorithms for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithm, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms. For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCA model is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.
Abstract: In observer-based approach for fault detection and isolation, two schemes are generally considered, namely the dedicated observer scheme (DOS) and the generalized observer scheme (GOS). DOS is a bank of observers sensitive to only one fault while GOS is composed of observers sensitive to all faults except one. In this paper a new sensor fault diagnosis approach named Reconstruction Observer Scheme (ROS) is proposed, which does not need any bank of observer, only one observer is used. The proposed method based on reconstruction of variables is used to generate a structured residuals for fault isolation. After the fault detection, the reconstruction is carried of all the variables. Reconstruction of a variable consists on the replacement of this variable to the input of the observer by its estimation. This operation eliminates fault effect when a faulty variable is reconstructed. The proposed approach is illustrated by an academic example.
Abstract: In this paper a new algorithms for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process monitoring. The proposed AKPCA algorithm combine two existing algorithm, the recursive weighted PCA (RWPCA) and the moving window kernel PCA algorithms.
For fault detection and isolation, a set of structured residuals is generated by using a partial AKPCA models. Each partial AKPCA model is performed on subsets of variables. The structured residuals are utilized in composing an isolation scheme, according to a properly designed incidence matrix. The results for applying this algorithm on the nonlinear time varying processes of the Tennessee Eastman shows its feasibility and advantageous performances.
