G. Vachtsevanos, F. L. Lewis, M. Roemer, A. Hess, and B. Wu, Intelligent Fault Diagnosis and Prognosis for Engineering Systems, 2006.
DOI : 10.1002/9780470117842

D. B. Jarrell, D. Sisk, and L. J. Bond, Prognostics and Condition-Based Maintenance: A New Approach to Precursive Metrics, Nucl. Technol, vol.145, pp.275-286, 2004.

J. W. Hines and A. Usynin, Current Computational Trends in Equipment Prognostics, International Journal of Computational Intelligence Systems, vol.1, issue.1, pp.94-102, 2008.
DOI : 10.2991/ijcis.2008.1.1.7

E. Zio, Prognostics and Health Management of Industrial Equipment
DOI : 10.4018/978-1-4666-2095-7.ch017
URL : https://hal.archives-ouvertes.fr/hal-00778377

T. Brotherton, G. Jahns, J. Jacobs, and D. Wroblewski, Prognosis of faults in gas turbine engines, IEEE Aerosp, Conf. Proc, vol.6, pp.163-171, 2000.

J. Luo, K. Pattipati, L. Qiao, and S. Chigusa, Model-based Prognostic Techniques Applied to a Suspension System, IEEE Trans. on Syst., Man, and Cybern, vol.38, issue.5, pp.1156-1168, 2008.

U. Pulkkinen, A stochastic model for wear prediction through condition monitoring, Operational reliability and systematic maintenance, pp.223-266, 1991.

A. Ray and S. Tangirala, A nonlinear stochastic model of fatigue crack dynamics, Probabilistic Engineering Mechanics, vol.12, issue.1, pp.33-40, 1997.
DOI : 10.1016/S0266-8920(96)00012-4

R. W. Swindeman and M. J. Swindeman, A comparison of creep models for nickel base alloys for advanced energy systems, International Journal of Pressure Vessels and Piping, vol.85, issue.1-2, pp.72-79, 2008.
DOI : 10.1016/j.ijpvp.2007.06.012

A. Doucet, On sequential simulation-based methods for Bayesian filtering, 1998.

A. Doucet, J. F. De-freitas, and N. J. Gordon, Sequential Monte Carlo methods in practice, 2001.
DOI : 10.1007/978-1-4757-3437-9

G. Kitagawa, Non-Gaussian state-space modeling of nonstationary time series, J. of the, Am. Stat. Assoc, vol.82, pp.1032-63, 1987.

F. Cadini, E. Zio, and D. Avram, Monte Carlo-based filtering for fatigue crack growth estimation, Prob. Engng Mech, pp.367-373, 2009.

T. Khan, Particle filter based prognosis study for predicting remaining useful life of steam generator tubing, 2011 IEEE Conference on Prognostics and Health Management, pp.20-23, 2011.
DOI : 10.1109/ICPHM.2011.6024323

M. S. Arulampalam, . Maskell, T. Gordon, and . Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, vol.50, issue.2, pp.174-88, 2002.
DOI : 10.1109/78.978374

M. A. Schwabacher, A Survey of Data-Driven Prognostics, Infotech@Aerospace, 2005.
DOI : 10.2514/6.2005-7002

K. Goebel, B. Saha, and A. Saxena, A Comparison of Three Data-Driven Algorithms for Prognostics, Proc. of the 62nd Meet, pp.119-131, 2008.

L. Peel, Data driven prognostics using a Kalman filter ensemble of neural network models, 2008 International Conference on Prognostics and Health Management, 2008.
DOI : 10.1109/PHM.2008.4711423

B. Saha, K. Goebel, and J. Christophersen, Comparison of prognostic algorithms for estimating remaining useful life of batteries, Transactions of the Institute of Measurement and Control, vol.31, issue.3-4, pp.31-34, 2009.
DOI : 10.1177/0142331208092030

F. Di-maio, J. Hu, P. Tse, K. Tsui, E. Zio et al., Ensemble-approaches for clustering health status of oil sand pumps, Expert Systems with Applications, vol.39, issue.5
DOI : 10.1016/j.eswa.2011.10.008

E. Zio and F. D. Maio, A Fuzzy Similarity-Based Method for Failure Detection and Recovery Time Estimation, Int. J. of Perform. Engng, vol.6, issue.5, pp.407-424, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00609189

R. Polikar, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, vol.6, issue.3, pp.21-45, 2006.
DOI : 10.1109/MCAS.2006.1688199

T. Heskes, Practical confidence and prediction intervals, Advances. Neural Information Processing Systems 9, pp.466-472, 1997.

R. Couturier and C. Escaravage, High temperature alloys for the HTGR Gas Turbine: Required properties and development needs, 2000.

P. Baraldi, F. Mangili, and E. Zio, A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics, IEEE Transactions on Reliability, vol.61, issue.4, 2011.
DOI : 10.1109/TR.2012.2221037
URL : https://hal.archives-ouvertes.fr/hal-00777665

E. Zio, A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes, IEEE Transactions on Nuclear Science, vol.53, issue.3, pp.1460-1478, 2006.
DOI : 10.1109/TNS.2006.871662

N. Gorjian, L. Ma, M. Mittinty, P. Yarlagadda, and Y. Sun, A review on degradation models in reliability analysis, Proc. of the 4th World Congr. on Engng Asset Manag, pp.28-30, 2009.
DOI : 10.1007/978-0-85729-320-6_42

M. Saez, N. Tauveron, T. Chataing, G. Geffraye, L. Briottet et al., Analysis of the turbine deblading in an HTGR with the CATHARE code, Nuclear Engineering and Design, vol.236, issue.5-6, pp.574-586, 2006.
DOI : 10.1016/j.nucengdes.2005.10.025

M. Orchard, A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis, 2007.

P. Li and V. Kadirkamanathan, Particle Filtering based Likelihood Ratio Approach to Fault Diagnosis in Nonlinear Stochastic Systems, IEEE Trans. On Systems Part C: Applications and Reviews, vol.31, issue.3, pp.337-343, 2001.

J. G. Carney, P. Cunningham, and U. Bhagwan, Confidence and prediction intervals for neural network ensembles, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), pp.1215-1218, 1999.
DOI : 10.1109/IJCNN.1999.831133