T. Stepinski, T. Uhl, and W. Staszewski, Advanced Structural Damage Detection: From Theory to Engineering Applications, 2013.

R. Chih-min-fan, S. Guo, C. Chang, and . Wei, SHEWMA: an end-of-line SPC scheme using wafer acceptance test data, IEEE Transactions on Semiconductor Manufacturing, vol.13, issue.3, pp.344-358, 2000.

W. Zhou, T. G. Habetler, and R. G. Harley, Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control, IEEE Transactions on Industrial Electronics, vol.55, issue.12, pp.4260-4269, 2008.

N. Kaistha, Incipient fault detection and isolation in a PWR plant using principal component analysis, Proceedings of the American Control Conference, vol.3, pp.12-24, 2001.

C. A. Lowry, W. H. Woodall, C. W. Champ, and S. E. Rigdon, A Multivariate Exponentially Weighted Moving Average Control Chart, vol.34, pp.46-53, 1992.

D. M. Hawkins and E. M. Maboudou-tchao, Multivariate Exponentially Weighted Moving Covariance Matrix, Technometrics, vol.50, issue.2, pp.155-166, 2008.

S. W. Cheng and K. Thaga, Single variables control charts : an overview, Quality and Reliability Engineering International, vol.22, issue.7, pp.811-820, 2006.

U. Kruger and L. Xie, Advances in Statistical Monitoring of Complex Multivariate Processes, 2012.

J. E. Jackson, A User's Guide to Principal Components, 1991.

X. Deng, X. Tian, and S. Chen, Modified kernel principal component analysis based on local structure analysis and its application to nonlinear process fault diagnosis, Chemometrics and Intelligent Laboratory Systems, vol.127, pp.195-209, 2013.

C. Sankavaram, B. Pattipati, K. Pattipati, Y. Zhang, M. Howell et al., Data-driven fault diagnosis in a hybrid electric vehicle regenerative braking system, IEEE Aerospace Conference, pp.1-11, 2012.

Y. Gao, T. Yang, N. Xing, and M. Xu, Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines, IEEE Conference on Industrial Electronics and Applications (ICIEA), 1984.

M. Hamadache and D. Lee, Principal Components Analysis based Fault Detection and Isolation for Electronic Throttle Control system, 12th International Conference on Control, Automation and Systems (ICCAS), pp.808-813, 2012.

A. A. Silva, A. M. Bazzi, and S. Gupta, Fault diagnosis in electric drives using machine learning approaches, IEEE International Electric Machines & Drives Conference (IEMDC), pp.722-726, 2013.

J. Yu, Fault detection using principal components-based gaussian mixture model for semiconductor manufacturing processes, IEEE Transactions on Semiconductor Manufacturing, vol.24, issue.3, pp.471-486, 2011.

J. Harmouche, C. Delpha, and D. Diallo, Incipient fault detection and diagnosis based on Kullback-Leibler divergence using Principal Component Analysis: Part I, vol.94, pp.278-287, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00862918

J. Kullaa, Detection, identification, and quantification of sensor fault in a sensor network, Mechanical Systems and Signal Processing, vol.40, pp.1208-221, 2013.

S. Wang and J. Cui, Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method, Applied Energy, vol.82, issue.3, pp.197-213, 2005.

S. A. Arogeti, D. Wang, C. B. Low, and M. Yu, Fault Detection Isolation and Estimation in a Vehicle Steering System, IEEE Transactions on Industrial Electronics, vol.59, issue.12, pp.4810-4820, 2012.

T. N. Pranatyasto and S. Qin, Sensor validation and process fault diagnosis for FCC units under MPC feedback, Control Engineering Practice, vol.9, issue.8, pp.877-888, 2001.

Z. Gao and S. X. Ding, Sensor fault reconstruction and sensor compensation for a class of nonlinear state-space systems via a descriptor system approach, Control Theory & Applications, IET, vol.1, pp.578-585, 2007.

M. Liu and P. Shi, Sensor fault estimation and tolerant control for Ito stochastic systems with a descriptor sliding mode approach, Automatica, vol.49, issue.5, pp.1242-1250, 2013.

Z. Gao and S. X. Ding, Fault estimation and fault-tolerant control for descriptor systems via proportional, multiple-integral and derivative observer design, Control Theory & Applications, IET, vol.1, pp.1208-1218, 2007.

J. Stoustrup and H. H. Niemann, Fault estimation -a standard problem approach, International journal of robust and nonlinear control, vol.12, issue.8, pp.649-673, 2002.

S. Valle, W. Li, and S. J. Qin, Comparison of multivariate statistical process control monitoring methods with applications to the Eastman challenge problem, Industrial & Engineering Chemistry Research, vol.38, issue.11, pp.4389-4401, 1999.

J. Trigeassou, Electrical Machines Diagnosis, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00782890

M. Kano, S. Hasebe, I. Hashimoto, and H. Ohno, A new multivariate statistical process monitoring method using principal component analysis, Computers & Chemical Engineering, vol.25, issue.7-8, pp.1103-1113, 2001.

J. Tajer, A. Makke, O. Salem, and A. Mehaoua, A comparison between divergence measures for network anomaly detection, 7th International Conference on Network and Service Management (CNSM), pp.1-5, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00844968

O. Salem, F. Nait-abdesselam, and A. Mehaoua, Anomaly detection in network traffic using Jensen-Shannon divergence, IEEE International Conference on Communications (ICC), pp.5200-5204, 2012.

W. Hung and M. Yang, On the J-divergence of intuitionistic fuzzy sets with its application to pattern recognition, Information Sciences, vol.178, pp.1641-1650, 2008.

K. S. Andrew, D. Jardine, D. Lin, and . Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance Mechanical Systems and Signal Processing, vol.20, pp.1483-1510, 2006.

D. Romano and M. Kinnaert, Robust fault detection and isolation based on the Kullback divergence, Fault Detection, Supervision and Safety of Technical Processes, vol.1, pp.426-431, 2006.

S. Kullback and R. A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.

M. Basseville, Distances Measures for Signal Processing and Pattern Recognition, Signal Processing, vol.18, issue.4, pp.349-369, 1989.

N. P. Van-der-aa, H. G. Ter-morsche, and R. R. Mattheij, Computation of eigenvalue and eigenvector derivatives for a general complexvalued eigensystem, Electronic Journal of Linear Algebra ELA, vol.16, pp.300-314, 2007.

M. Thomas, J. A. Cover, and . Thomas, Elements of Information Theory, 2006.

G. Casella and R. L. Berger, Statistical Inference, 2002.

J. Y. Park, M. B. Wakin, and A. C. Gilbert, Modal Analysis With Compressive Measurements, IEEE Transactions on Signal Processing, vol.62, issue.7, pp.1655-1670, 2014.

J. Kullaa, Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring, Mechanical Systems and Signal Processing, vol.25, pp.2976-2989, 2011.

D. A. Tibaduiza, M. A. Torres-arredondo, L. E. Mujica, J. Rodellar, and C. P. Fritzen, Study of two unsupervised data driven statistical methodologies for detecting and classifying damages in structural health monitoring, Mechanical Syst. and Sig. Proc, vol.41, pp.467-484, 2013.

J. Harmouche, C. Delpha, and D. Diallo, Incipient fault detection and diagnosis based on Kullback-Leibler divergence using Principal Component Analysis: Part II, vol.109, pp.334-344, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01100666