89 3.5.1 Méthodes utilisant la projection dans l'espace résiduel . . . . . 89 3.5.2 Méthodes utilisant la projection dans l'espace principal, p.92 ,
effet de défauts sur les résidus générés, des défauts sont ajoutés aux données de la matrice Z s aux instants suivants : ? des instants 10à10à 24 pour la variable z s | 1 (intervalle I 1 ), ? des instants 35à35à 49 pour les variables z s | 8 (intervalle I 2 ), ? des instants 60à60à 74 pour les variables z s | 4 et z s | 8 (intervalle I 3 ), ? des instants 85à85à 99 ,
The complexity of computing the MCD-estimator, Theoretical Computer Science, vol.326, issue.1-3, pp.383-398, 2004. ,
DOI : 10.1016/j.tcs.2004.08.005
Sur l'usage de la validation croisée en analyse en composantes principales, pp.71-76, 1993. ,
Some Theorems on Quadratic Forms Applied in the Study of Analysis of Variance Problems, I. Effect of Inequality of Variance in the One-Way Classification, The Annals of Mathematical Statistics, vol.25, issue.2, pp.290-320, 1954. ,
DOI : 10.1214/aoms/1177728786
The effect of insufficient excitation in PCA estimation, Proceedings of The American Control Conference, pp.2707-2712, 2002. ,
Noise-induced bias in last principal component modeling of linear system, Journal of Process Control, vol.14, issue.4, pp.365-376, 2004. ,
DOI : 10.1016/j.jprocont.2003.06.002
Projections révélatrices contrôlées : Groupements et structures diverses, pp.37-58, 2003. ,
Méthodologie d'optimisation dynamique et de commande optimale des petites stations d'´ epurationàepurationà boues activées, 2001. ,
Industrial implementation of on-line multivariate quality control, Chemometrics and Intelligent Laboratory Systems, vol.88, issue.2, pp.143-153, 2007. ,
DOI : 10.1016/j.chemolab.2007.02.005
Analytical redundancy and the design of robust failure detection systems, IEEE Transactions on Automatic Control, vol.29, issue.7, pp.603-614, 1984. ,
DOI : 10.1109/TAC.1984.1103593
A Fast Algorithm for Robust Principal Components Based on Projection Pursuit, COMPSTAT 96, pp.211-216, 1996. ,
DOI : 10.1007/978-3-642-46992-3_22
High breakdown estimators for principal components: the projection-pursuit approach revisited, Journal of Multivariate Analysis, vol.95, issue.1, pp.206-226, 2005. ,
DOI : 10.1016/j.jmva.2004.08.002
Robust statistics in data analysis -a review. basic concepts, pp.203-219, 2007. ,
Model-based Fault Diagnosis Techniques, 2008. ,
DOI : 10.1007/978-1-4471-4799-2
A characterization of parity space and its application to robust fault detection, IEEE Transactions on Automatic Control, vol.44, issue.2, pp.337-343, 1999. ,
DOI : 10.1109/9.746262
A Festschrift for Eric Lehmann, chapter The notion of breakdown point, 1983. ,
Subspace approach to multidimensional fault identification and reconstruction, AIChE Journal, vol.2, issue.8, pp.441813-1831, 1998. ,
DOI : 10.1002/0471725331
Identification of faulty sensors using principal component analysis, AIChE Journal, vol.42, issue.10, pp.422797-2812, 1996. ,
DOI : 10.1002/aic.690421011
A unified geometric approach to process and sensor fault identification and reconstruction, Computers & Chemical Engineering, vol.22, issue.7-8, pp.927-943, 1998. ,
DOI : 10.1016/S0098-1354(97)00277-9
Selection of components in principal component analysis: A comparison of methods, Computational Statistics & Data Analysis, vol.19, issue.6, pp.669-682, 1995. ,
DOI : 10.1016/0167-9473(94)00020-J
Outlier identification in high dimensions, Computational Statistics & Data Analysis, vol.52, issue.3, pp.1694-1711, 2008. ,
DOI : 10.1016/j.csda.2007.05.018
Modelling for fault detection and isolation versus modelling for control, Mathematics and Computers in Simulation, vol.53, pp.4-6259, 2000. ,
Activated sludge wastewater treatment plant modelling and simulation: state of the art, Environmental Modelling & Software, vol.19, issue.9, pp.763-783, 2004. ,
DOI : 10.1016/j.envsoft.2003.03.005
Design of optimal structured residuals from partial principal component models for fault diagnosis in linear systems, Journal of Process Control, vol.15, issue.5, pp.585-603, 2005. ,
DOI : 10.1016/j.jprocont.2004.10.005
Isolation enhanced principal component analysis, AIChE Journal, vol.41, issue.2, p.45, 1999. ,
DOI : 10.1002/aic.690450213
Principal Component Analysis and Parity Relations - A Strong Duality, IFAC Proceedings Volumes, vol.30, issue.18, pp.837-842, 1997. ,
DOI : 10.1016/S1474-6670(17)42503-1
Isolation enhanced principal component analysis. 3rd IFAC Workshop on On-line Fault Detection and Supervision in the Chemical Process Industries, 1998. ,
Matrix computations, 1996. ,
Modélisation et estimation robuste pour un procédé boues activées en alternance de phases, 2002. ,
Activated Sludge Model No. 3, Water Science and Technology, vol.39, issue.1, pp.183-193, 1999. ,
Statistical signal processing approaches to fault detection, Annual Reviews in Control, vol.31, issue.1, pp.41-54, 2007. ,
DOI : 10.1016/j.arcontrol.2007.02.004
A comparison of two methods for stochastic fault detection: the parity space approach and principal components analysis, Proceedings of SYSID, 2003. ,
DOI : 10.1016/S1474-6670(17)34898-X
Détection et Localisation de Défauts par Analyse en Composantes Principales, Thèse de doctorat, 2003. ,
An improved PCA scheme for sensor FDI: Application to an air quality monitoring network, Journal of Process Control, vol.16, issue.6, pp.625-634, 2006. ,
DOI : 10.1016/j.jprocont.2005.09.007
URL : https://hal.archives-ouvertes.fr/hal-00093739
The Detection of Errors in Multivariate Data Using Principal Components, Journal of the American Statistical Association, vol.64, issue.3, pp.340-344, 1974. ,
DOI : 10.2307/2346776
Activated Sludge Model No, IAWPRC Scientific and Technical Reports, 1987. ,
Activated Sludge Model No. 2, IAWPRC Scientific and Technical Reports, 1994. ,
Activated Sludge Model No.2d, ASM2d. Water Science and Technology, vol.39, issue.1, pp.165-182, 1999. ,
Determination of the number of principal components for disturbance detection and isolation, Proceedings of 1994 American Control Conference, ACC '94, 1994. ,
DOI : 10.1109/ACC.1994.752265
Process identification based on last principal component analysis, Journal of Process Control, vol.11, issue.1, pp.19-33, 2001. ,
DOI : 10.1016/S0959-1524(99)00062-1
Fault Isolation by Partial PCA and Partial NLPCA, 14th Triennial world congress, IFAC'99, pp.545-550, 1999. ,
DOI : 10.1016/S1474-6670(17)57305-X
Robust Estimation of a Location Parameter, The Annals of Mathematical Statistics, vol.35, issue.1, pp.73-101, 1964. ,
DOI : 10.1214/aoms/1177703732
ROBPCA: A New Approach to Robust Principal Component Analysis, Technometrics, vol.47, issue.1, pp.64-79, 2005. ,
DOI : 10.1198/004017004000000563
A fast method for robust principal components with applications to chemometrics, Chemometrics and Intelligent Laboratory Systems, vol.60, issue.1-2, pp.101-111, 2002. ,
DOI : 10.1016/S0169-7439(01)00188-5
Modelling Aspects of Wastewater Treatment Processes, Thèse de doctorat, Lund Institute of Technology (LTH), 1996. ,
The COST benchmark simulation model???current state and future perspective, Control Engineering Practice, vol.12, issue.3, pp.299-304, 2004. ,
DOI : 10.1016/j.conengprac.2003.07.001
Process monitoring in principal component subspace: part 2. fault identification and isolation study *, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), pp.6087-6092, 2004. ,
DOI : 10.1109/ICSMC.2004.1401353
A new multivariate statistical process monitoring method using principal component analysis, Computers & Chemical Engineering, vol.25, issue.7-8, pp.1103-1113, 2001. ,
DOI : 10.1016/S0098-1354(01)00683-4
Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry, Computers & Chemical Engineering, vol.32, issue.1-2, pp.12-24, 2008. ,
DOI : 10.1016/j.compchemeng.2007.07.005
Multivariate statistical monitoring of process operating performance, The Canadian Journal of Chemical Engineering, vol.27, issue.1, pp.35-47, 1991. ,
DOI : 10.1080/14786440109462720
Improved principal component monitoring of large-scale processes, Journal of Process Control, vol.14, issue.8, pp.879-888, 2004. ,
DOI : 10.1016/j.jprocont.2004.02.002
Disturbance detection and isolation by dynamic principal component analysis, Chemometrics and Intelligent Laboratory Systems, vol.30, issue.1, pp.179-196, 1995. ,
DOI : 10.1016/0169-7439(95)00076-3
Detection of process model changes in PCA based performance monitoring, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), pp.2719-2724, 2002. ,
DOI : 10.1109/ACC.2002.1025198
Méthodes multivariables pour la caractérisation des eaux usées, Thèse de doctorat, 2003. ,
Sensor fault identification based on timelagged pca in dynamic processes, pp.165-178, 2004. ,
Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo, Journal of the American Statistical Association, vol.4, issue.391, pp.759-766, 1985. ,
DOI : 10.1214/aos/1176343347
Consistent dynamic PCA based on errors-in-variables subspace identification, Journal of Process Control, vol.11, issue.6, pp.661-678, 2001. ,
DOI : 10.1016/S0959-1524(00)00041-X
Fault reconstruction in linear dynamic systems using multivariate statistics, IEE Proceedings - Control Theory and Applications, vol.153, issue.4, pp.437-446, 2006. ,
DOI : 10.1049/ip-cta:20040385
Statistical Monitoring of Dynamic Multivariate Processes ??? Part 2. Identifying Fault Magnitude and Signature, Industrial & Engineering Chemistry Research, vol.45, issue.5, pp.1677-1688, 2006. ,
DOI : 10.1021/ie060017b
Optimal Structured Residual Approach for Improved Faulty Sensor Diagnosis, Industrial & Engineering Chemistry Research, vol.44, issue.7, pp.2117-2124, 2005. ,
DOI : 10.1021/ie049213d
Control and estimation strategies applied to the activated sludge process, Thèse de doctorat, 1997. ,
System identification : theory for the user, 1987. ,
Statistical process control of multivariate processes, Control Engineering Practice, vol.3, issue.3, pp.403-414, 1995. ,
DOI : 10.1016/0967-0661(95)00014-L
Factor Analysis in Chemistry, 1991. ,
Principal Components and Orthogonal Regression Based on Robust Scales, Technometrics, vol.47, issue.3, p.47, 2005. ,
DOI : 10.1198/004017005000000166
Robust Statistics : Theory and Methods, 2006. ,
DOI : 10.1002/0470010940
Contribution plots : A missing link in multivariate quality control, Applied Mathematics and Computer Science, vol.8, issue.4, pp.775-792, 1998. ,
Model identification and error covariance matrix estimation from noisy data using PCA, Control Engineering Practice, vol.16, issue.1, pp.146-155, 2008. ,
DOI : 10.1016/j.conengprac.2007.04.006
A critique of the use of pca for fault detection and diagnosis, 1998. ,
Statistical process monitoring : basics and beyond, Journal of Chemometrics, vol.17, pp.8-9480, 2003. ,
Determining the number of principal components for best reconstruction, Journal of Process Control, vol.10, issue.2-3, pp.245-250, 2000. ,
DOI : 10.1016/S0959-1524(99)00043-8
Detection and identification of faulty sensors in dynamic processes, AIChE Journal, vol.30, issue.7, pp.471581-1593, 2001. ,
DOI : 10.1002/0471725331
Detection, identification and reconstruction of faulty sensors with maximized sensitivity, American Institute Of Chemical Engineers Journal, vol.45, issue.9, pp.1963-1976, 1999. ,
Monitoring Wastewater Treatment Systems, Thèse de doctorat, Lund Institute of Technology (LTH), 1998. ,
Robust regression and outliers detection, 1987. ,
DOI : 10.1002/0471725382
Robustness and Outlier Detection in Chemometrics, Critical Reviews in Analytical Chemistry, vol.59, issue.3-4, pp.221-242, 2006. ,
DOI : 10.1016/j.chemolab.2004.06.003
A Fast Algorithm for the Minimum Covariance Determinant Estimator, Technometrics, vol.35, issue.3, pp.212-223, 1999. ,
DOI : 10.1080/01621459.1994.10476821
Least Median of Squares Regression, Journal of the American Statistical Association, vol.53, issue.388, pp.871-880, 1984. ,
DOI : 10.1214/aos/1176345451
On fault detection using dynamic pca with varying input excitation, Workshop on Advanced Control and Diagnosis, 2004. ,
A study on the number of principal components and sensitivity of fault detection using PCA, Computers & Chemical Engineering, vol.31, issue.9, pp.311035-1046, 2007. ,
DOI : 10.1016/j.compchemeng.2006.09.004
Exploratory Data Analysis, 1977. ,
Subspace identification for linear systems- Theory, implementation, applications, 1996. ,
Process monitoring in principal component subspace : part 1 -fault reconstruction study, IEEE International Conference on Systems, Man and Cybernetics, pp.5119-5124, 2004. ,
Number selection of principal components with optimized process monitoring performance, 43rd IEEE Conference on Decision and Control, 2004. ,
A new subspace identification approach based on principal component analysis, Journal of Process Control, vol.12, issue.8, pp.841-855, 2002. ,
DOI : 10.1016/S0959-1524(02)00016-1
Maximum likelihood principal component analysis, Journal of Chemometrics, vol.11, issue.4, pp.339-366, 1997. ,
DOI : 10.1002/(SICI)1099-128X(199707)11:4<339::AID-CEM476>3.0.CO;2-L
URL : http://myweb.dal.ca/pdwentze/papers/a34.pdf
StandardizedQ-statistic for improved sensitivity in the monitoring of residuals in MSPC, Journal of Chemometrics, vol.35, issue.4, pp.335-349, 2000. ,
DOI : 10.1021/ie9502594
The process chemometrics approach to process monitoring and fault detection, Journal of Process Control, vol.6, issue.6, pp.329-348, 1996. ,
DOI : 10.1016/0959-1524(96)00009-1
Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models, Technometrics, vol.35, issue.4, pp.397-405, 1978. ,
DOI : 10.1016/S0021-9673(01)85348-6
Statistical Monitoring of Dynamic Multivariate Processes Part 1. Modeling Autocorrelation and Cross-correlation, Industrial & Engineering Chemistry Research, vol.45, issue.5, pp.1659-1676, 2006. ,
DOI : 10.1021/ie050583r
High breakdown-point and high efficiency robust estimates for regression . The Annals of Statistics, pp.642-656, 1987. ,
Statistical and causal model-based approaches to fault detection and isolation, AIChE Journal, vol.12, issue.9, pp.1813-1824, 2000. ,
DOI : 10.1002/0471725331
Reconstruction-Based Fault Identification Using a Combined Index, Industrial & Engineering Chemistry Research, vol.40, issue.20, pp.4403-4414, 2001. ,
DOI : 10.1021/ie000141+