A. Hess, G. Calvello, and P. Frith, Challenges, issues, and lessons learned chasing the 'Big P': real predictive prognostics Part 1, Proc IEEE Aerosp Conf, 2006.

L. Tang, G. Kacprzynski, K. Goebel, and G. Vachtsevanos, Methodologies for uncertainty management in prognostics, 2009 IEEE Aerospace conference, 2009.
DOI : 10.1109/AERO.2009.4839668

W. Li and H. Pham, An Inspection-Maintenance Model for Systems With Multiple Competing Processes, IEEE Transactions on Reliability, vol.54, issue.2, pp.318-345, 2005.
DOI : 10.1109/TR.2005.847264

R. Liu, L. Ma, R. Kang, and N. Wang, The modeling method on failure prognostics uncertainties in maintenance policy decision process, The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety, pp.12-15, 2011.
DOI : 10.1109/ICRMS.2011.5979378

A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel, Evaluating prognostics performance for algorithms incorporating uncertainty estimates, 2010 IEEE Aerospace Conference, 2010.
DOI : 10.1109/AERO.2010.5446828

A. Urbina, S. Mahadevan, and T. Paez, Quantification of margins and uncertainties of complex systems in the presence of aleatoric and epistemic uncertainty, Reliability Engineering & System Safety, vol.96, issue.9, pp.1114-1139, 2011.
DOI : 10.1016/j.ress.2010.08.010

E. Zio, Prognostics and Health Management of Industrial Equipment, Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, p.2012
DOI : 10.4018/978-1-4666-2095-7.ch017

URL : https://hal.archives-ouvertes.fr/hal-00778377

P. Baraldi, F. Cadini, F. Mangili, and E. Zio, Model-based and data-driven prognostics under different available information. Probab Eng Mech, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00934560

J. Luo, K. Pattipati, L. Qiao, and S. Chigusa, Model-based prognostic techniques applied to a suspension system, IEEE Trans Syst, Man, and Cybern ? Part C: Appl and Rev, vol.38, issue.5, pp.1156-68, 2008.

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

E. Myötyri, U. Pulkkinen, and K. Simola, Application of stochastic filtering for lifetime prediction, Reliability Engineering & System Safety, vol.91, issue.2, pp.200-208, 2006.
DOI : 10.1016/j.ress.2005.01.002

M. Arulampalam, S. Maskell, N. Gordon, and T. 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

B. Anderson and J. Moore, Optimal Filtering, IEEE Transactions on Systems, Man, and Cybernetics, vol.12, issue.2, 1979.
DOI : 10.1109/TSMC.1982.4308806

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

D. Crisan and A. Doucet, A survey of convergence results on particle filtering methods for practitioners, IEEE Transactions on Signal Processing, vol.50, issue.3, pp.736-782, 2002.
DOI : 10.1109/78.984773

P. Djuric, J. Kotecha, J. Zhang, Y. Huang, T. Ghirmai et al., Particle Filtering, IEEE Signal Processing Magazine, vol.20, issue.5, pp.19-38, 2003.
DOI : 10.1109/MSP.2003.1236770

URL : https://hal.archives-ouvertes.fr/hal-01437041

M. Pitt and N. Shephard, Filtering via Simulation: Auxiliary Particle Filters, Journal of the American Statistical Association, vol.24, issue.446, pp.590-599, 1999.
DOI : 10.1016/0005-1098(71)90097-5

M. Marseguerra and E. Zio, Monte Carlo simulation for model-based fault diagnosis in dynamic systems, Reliability Engineering & System Safety, vol.94, issue.2, pp.180-186, 2009.
DOI : 10.1016/j.ress.2008.02.013

F. Cadini, E. Zio, and A. D. , Model-based Monte Carlo state estimation for condition-based component replacement, Reliability Engineering & System Safety, vol.94, issue.3, pp.752-760, 2009.
DOI : 10.1016/j.ress.2008.08.003

F. Cadini, E. Zio, and A. D. , Monte Carlo-based filtering for fatigue crack growth estimation, Probabilistic Engineering Mechanics, vol.24, issue.3, pp.367-73, 2009.
DOI : 10.1016/j.probengmech.2008.10.002

E. Zio and G. Peloni, Particle filtering prognostic estimation of the remaining useful life of nonlinear components, Reliability Engineering & System Safety, vol.96, issue.3, pp.403-412, 2011.
DOI : 10.1016/j.ress.2010.08.009

URL : https://hal.archives-ouvertes.fr/hal-00609502

E. Zio and M. Compare, Evaluating maintenance policies by quantitative modeling and analysis, Reliability Engineering & System Safety, vol.109, pp.53-65, 2013.
DOI : 10.1016/j.ress.2012.08.002

URL : https://hal.archives-ouvertes.fr/hal-00734272

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

T. Heskes, Practical confidence and prediction intervals in Advances, Neural Information Processing Systems 9, pp.466-72, 1997.

Y. Raviv and N. Intrator, Bootstrapping with Noise: An Effective Regularization Technique, Connection Science, vol.8, issue.3-4, pp.3-4355, 1996.
DOI : 10.1080/095400996116811

R. Couturier and C. Escaravage, High temperature alloys for the HTGR gas turbine: Required properties and development needs, Proc IAEA Tech Comm Meet on Gas Turbine Power Convers Syst for Modular HTGRs, 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, 2012.
DOI : 10.1109/TR.2012.2221037

URL : https://hal.archives-ouvertes.fr/hal-00777665

A. Saxena, J. Celaya, B. Saha, S. Saha, and K. Goebel, Metrics for offline evaluation of prognostic performance, Int. J. of PHM, vol.1, pp.1-20, 2010.

A. Usynin, J. Hines, and A. Urmanov, Uncertain failure thresholds in cumulative damage models, 2008 Annual Reliability and Maintainability Symposium, pp.334-340, 2008.
DOI : 10.1109/RAMS.2008.4925818

P. Wang and D. Coit, Reliability and Degradation Modeling with Random or Uncertain Failure Threshold, 2007 Proceedings, Annual Reliability and Maintainability Sympsoium, pp.334-340, 2008.
DOI : 10.1109/RAMS.2007.328107

M. Orchard, G. Kacprzynski, K. Goebel, B. Saha, and G. Vachtsevanos, Advances in uncertainty representation and management for particle filtering applied to prognostics, 2008 International Conference on Prognostics and Health Management, 2008.
DOI : 10.1109/PHM.2008.4711433

J. Kotecha and P. Djuric, Gaussian sum particle filtering, IEEE Transactions on Signal Processing, vol.51, issue.10, pp.2592-601, 2003.
DOI : 10.1109/TSP.2003.816754

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.3-4293, 2009.
DOI : 10.1177/0142331208092030

H. Lopes, C. Carvalho, M. West, D. P. Dellaportas, P. Polson et al., Online Bayesian learning in dynamic models: an illustrative introduction to particle methods, Bayesian Dynamic Modelling Bayesian Inference and Markov Chain Monte Carlo: In Honour of
DOI : 10.1093/acprof:oso/9780199695607.003.0011

X. Wang, M. Rabiei, J. Hurtado, M. Modarres, and P. Hoffman, A probabilistic-based airframe integrity management model, Reliability Engineering & System Safety, vol.94, issue.5, pp.932-973, 2009.
DOI : 10.1016/j.ress.2008.10.010

E. A. 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.1460-78, 2006.
DOI : 10.1109/TNS.2006.871662

B. Efron and R. Tibshirani, An Introduction to the Bootstrap, 1993.
DOI : 10.1007/978-1-4899-4541-9

J. 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), 1999.
DOI : 10.1109/IJCNN.1999.831133

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

T. Carter, Common failures in gas turbine blades. Eng Failure Analysis, pp.237-284, 2005.

T. Dunn, L. Lommers, and V. Tangira, Preliminary safety evaluation of the gas turbine-modular Helium reactor (GT-MHR), Proc Int Top Meet Adv React Saf, 1994.

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.574-86, 2006.
DOI : 10.1016/j.nucengdes.2005.10.025

M. Made, A. Mirmiran, and T. Walter, Local damage assessment of turbine missile impact on composite and multiple barriers, Nucl Eng and Des, vol.178, pp.145-56, 1997.

N. Goel, A. Kumar, V. Narasimhan, A. Nayak, and A. Srivastava, Health risk assessment and prognosis of gas turbine blades by simulation and statistical methods. Can Conf on Electr and Comput Eng, 2007.

A. Flotow, M. Mercadal, and P. Tappert, Health monitoring and prognostics of blades and disks with blade tip sensors, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484), 2000.
DOI : 10.1109/AERO.2000.877917

D. Ye, F. Duan, H. Guo, Y. Li, and K. Wang, Turbine blade tip clearance measurement using a skewed dual-beam fiber optic sensor, Optical Engineering, vol.51, issue.8, p.51, 2012.
DOI : 10.1117/1.OE.51.8.081514

A. Steiner, Techniques for blade tip clearance measurements with capacitive probes, Measurement Science and Technology, vol.11, issue.7, pp.865-874, 2000.
DOI : 10.1088/0957-0233/11/7/303

M. Dowell, G. Sylvester, R. Krupp, and G. Zipfel, Progress in turbomachinery prognostics and health management via eddy-current sensing, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484), pp.133-176, 2000.
DOI : 10.1109/AERO.2000.877888

D. Kwapisz, M. Hafner, and R. Rajamani, application of microwave sensing to blade health monitoring, Proc 1st Eur Conf of PHM Soc, pp.1-8, 2012.

R. Polikar, A. Topalis, D. Green, J. Kounios, and C. Clark, Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease, Computers in Biology and Medicine, vol.37, issue.4, pp.542-58, 2007.
DOI : 10.1016/j.compbiomed.2006.08.012

T. Carter, Common failures in gas turbine blades, Engineering Failure Analysis, vol.12, issue.2, pp.237-284, 2005.
DOI : 10.1016/j.engfailanal.2004.07.004

R. Swindeman and M. 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.72-81, 2008.
DOI : 10.1016/j.ijpvp.2007.06.012

T. Li, T. Sattar, and S. Sun, Deterministic resampling: Unbiased sampling to avoid sample impoverishment in particle filters, Signal Processing, vol.92, issue.7, pp.1637-1682, 2012.
DOI : 10.1016/j.sigpro.2011.12.019

E. Zio, D. Maio, and F. , A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system, Reliability Engineering & System Safety, vol.95, issue.1, pp.49-57, 2010.
DOI : 10.1016/j.ress.2009.08.001