C. J. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

R. Chevalier, D. Provost, and R. Seraoui, Assessment of Statistical and Classification Models For Monitoring EDF's Assets, Sixth American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, 2009.

D. Maio, F. Baraldi, P. Zio, E. Seraoui, and R. , Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods, IEEE Transactions on Reliability, vol.62, issue.4, pp.833-845, 2013.
DOI : 10.1109/TR.2013.2285033

URL : https://hal.archives-ouvertes.fr/hal-00934649

G. Guglielmi, T. Parisini, and G. Rossi, Keynote paper: Fault diagnosis and neural networks: A power plant application, Control Engineering Practice, vol.3, issue.5, pp.601-620, 1995.
DOI : 10.1016/0967-0661(95)00037-U

Z. Hameed, Y. S. Hong, Y. M. Cho, S. H. Ahn, and C. K. Song, Condition monitoring and fault detection of wind turbines and related algorithms: A review, Renewable and Sustainable Energy Reviews, vol.13, issue.1, pp.1-39, 2009.
DOI : 10.1016/j.rser.2007.05.008

M. F. Harkat, S. Djelel, N. Doghmane, and M. Benouaret, Sensor fault detection, isolation and reconstruction using nonlinear principal component analysis, International Journal of Automation and Computing, vol.53, issue.5, pp.149-155, 2007.
DOI : 10.1007/s11633-007-0149-6

J. W. Hines and D. Garvey, Process and equipment monitoring methodologies applied to sensor calibration monitoring, Quality and Reliability Engineering International, vol.116, issue.1, pp.123-135, 2007.
DOI : 10.1002/qre.818

L. B. Jack and A. K. Nandi, FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS, Mechanical Systems and Signal Processing, pp.373-390, 2002.
DOI : 10.1006/mssp.2001.1454

K. Müller, M. , S. Rätsch, G. Tsuda, K. Schölkopf et al., An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks, vol.12, issue.2, pp.181-201, 2001.
DOI : 10.1109/72.914517

K. Nabeshima, T. Suzudo, K. Suzuki, and E. Turcan, Real-time Nuclear Power Plant Monitoring with Neural Network, Journal of Nuclear Science and Technology, vol.1, issue.1, pp.93-100, 1998.
DOI : 10.1080/18811248.1998.9733829

V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, A review of process fault detection and diagnosis, Computers & Chemical Engineering, vol.27, issue.3, pp.293-311, 2003.
DOI : 10.1016/S0098-1354(02)00160-6

V. Venkatasubramanian, R. Rengaswamy, and S. N. Kavuri, A review of process fault detection and diagnosis, Computers & Chemical Engineering, vol.27, issue.3, pp.313-326, 2003.
DOI : 10.1016/S0098-1354(02)00161-8

V. Venkatasubramanian, R. Rengaswamy, S. N. Kavuri, and K. Yin, A review of process fault detection and diagnosis, Computers & Chemical Engineering, vol.27, issue.3, pp.327-346, 2003.
DOI : 10.1016/S0098-1354(02)00162-X

A. Widodo and B. Yang, Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, pp.2560-2574, 2007.
DOI : 10.1016/j.ymssp.2006.12.007