R. B. Abernethy, The New Weibull Handbook, 1996.

L. Angstenberger, Dynamic fuzzy pattern recognition International series in intelligent technologies, 2001.

M. S. 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.174-188, 2002.
DOI : 10.1109/78.978374

S. E. Aumeier, B. Alpay, J. C. Lee, and A. Z. Akcasu, Probabilistic Techniques for Diagnosis of Multiple Component Degradations, Nuclear Science and Engineering, vol.153, pp.101-123, 2006.

V. Bagdonavicius and M. Nikulin, Estimation in Degradation Models with Explanatory Variables, Lifetime Data Analysis, vol.7, issue.1, pp.85-103, 2000.
DOI : 10.1023/A:1009629311100

P. Baraldi, R. Razavi-far, and E. Zio, A Method for Estimating the Confidence in the Identification of Nuclear Transients by a Bagged Ensemble of FCM Classifiers. Paper presented at the 7 th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls and Human Machine Interface Technology, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00721035

P. Baraldi, R. Razavi-far, and E. Zio, Bagged ensemble of Fuzzy C-Means classifiers for nuclear transient identification, Annals of Nuclear Energy, vol.38, issue.5, pp.1161-1171, 2011.
DOI : 10.1016/j.anucene.2010.12.009

URL : https://hal.archives-ouvertes.fr/hal-00609529

P. Baraldi, E. Zio, M. Compare, G. Rossetti, and A. Despujols, A Novel approach to Model the Degradation of components in electrical production plants, Paper presented at the European Safety and Reliability Conference, 2009.

D. Bendell, D. Wightman-and, and E. Walker, Applying proportional hazards modelling in reliability, Reliability Engineering & System Safety, vol.34, issue.1, pp.35-53, 1991.
DOI : 10.1016/0951-8320(91)90098-R

C. Bérenguer, A. Grall, B. Castanier, M. Cottam, D. Harvey et al., Simulation and evaluation of condition-based maintenance policies for multi-component continuous-state deteriorating systems, Proceedings of the ESREL 2000 Foresight and Precaution conference, pp.275-82, 2000.

M. Bigerelle and A. Iost, Bootstrap analysis of FCGR, application to the Paris relationship and to lifetime prediction, International Journal of Fatigue, vol.21, issue.4, pp.299-307, 1999.
DOI : 10.1016/S0142-1123(98)00076-0

V. V. Bolotin and A. A. Shipkov, A model of the environmentally affected growth of fatigue cracks, Journal of Applied Mathematics and Mechanics, vol.62, issue.2, p.313, 1998.
DOI : 10.1016/S0021-8928(98)00037-9

C. S. Byington, M. J. Roemer, and P. W. Kalgren, Verification and Validation of Diagnostic/Prognostic Algorithms. Paper presented at Machinery Fault Prevention Technology Conference (MFPT 59), 2005.

F. Cadini, E. Zio, and D. Avram, Monte Carlo-based filtering for fatigue crack growth estimation, Probabilistic Engineering Mechanics, vol.24, issue.3, pp.367-373, 2009.
DOI : 10.1016/j.probengmech.2008.10.002

G. Chryssolouris, M. Lee, and A. Ramsey, Confidence interval prediction for neural network models, IEEE Transactions on Neural Networks, vol.7, issue.1, pp.229-232, 1996.
DOI : 10.1109/72.478409

J. Coble and J. W. Hines, Fusing Data Sources for Optimal Prognostic Parameter Selection. Paper presented at the 6 th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls and Human Machine Interface Technology, 2009.

J. Coble and J. W. Hines, Analysis of Prognostic Opportunities in Power Industry with Demonstration. Paper presented at the 6 th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls and Human Machine Interface Technology, 2009.

D. Cox, Regression Models and Life-Tables, Journal of the Royal Statistics B, vol.34, pp.187-202, 1972.
DOI : 10.1007/978-1-4612-4380-9_37

D. R. Cox and D. Oakes, Analysis of Survival Data, 1984.

J. De-kleer and B. C. Williams, Diagnosing multiple faults, Artificial Intelligence, vol.32, issue.1, pp.97-130, 1987.
DOI : 10.1016/0004-3702(87)90063-4

E. Deloux, B. Castanier, and C. Bérenguer, Condition-based maintenance approaches for deteriorating system influenced by environmental conditions. Paper presented at the European Safety and Reliability Conference, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00363190

A. Doucet, J. F. De-freitas, and N. J. Gordon, An Introduction to Sequential Monte Carlo Methods, Sequential Monte Carlo in Practice, 2001.
DOI : 10.1007/978-1-4757-3437-9_1

D. Dubois, H. Prade, and C. Testemale, Weighted Fuzzy Pattern Matching. Fuzzy Sets and Systems, pp.313-331, 1988.
DOI : 10.1016/0165-0114(88)90038-3

J. E. Dzakowic and G. S. Valentine, Advanced Techniques for the Verification and Validation of Prognostics and Health Management Capabilities. Paper presented at Machinery Failure Prevention Technology (MFPT 60), 2007.

B. Efron, Bootstrap Methods: Another Look at the Jackknife, The Annals of Statistics, vol.7, issue.1, pp.1-26, 1979.
DOI : 10.1214/aos/1176344552

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

N. Gebraeel, A. Elwany, and J. Pan, Residual Life Predictions in the Absence of Prior Degradation Knowledge, IEEE Transactions on Reliability, vol.58, issue.1, pp.106-117, 2009.
DOI : 10.1109/TR.2008.2011659

K. Goebel and P. Bonissone, Prognostic information fusion for constant load systems, 2005 7th International Conference on Information Fusion, pp.1247-1255, 2005.
DOI : 10.1109/ICIF.2005.1592000

A. Grall, C. Bérenguer, and C. Chu, Optimal dynamic inspection/replacement planning in condition-based maintenance, Proceedings of the European safety and reliability conference ESREL'98, pp.381-388, 1998.

A. Heng, A. Tan, J. Mathew, N. Montgomery, D. Banjevic et al., Intelligent condition-based prediction of machinery reliability, Mechanical Systems and Signal Processing, vol.23, issue.5, pp.1600-1614, 2009.
DOI : 10.1016/j.ymssp.2008.12.006

J. W. Hines and D. R. Garvey, Data Based Fault Detection, Diagnosis and Prognosis of Oil Drill Steering Systems. Paper presented at the Maintenance and Reliability Conference, 2007.

J. W. Hines, J. Garvey, J. Preston, and A. Usynin, Empirical Methods for Process and Equipment Prognostics. Paper presented at 53 rd Annual Reliability and Maintainability Symposium, 2008.

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

J. A. Hontelez, H. H. Burger, and D. J. Wijnmalen, Optimum condition-based maintenance policies for deteriorating systems with partial information, Reliability Engineering & System Safety, vol.51, issue.3, pp.267-274, 1996.
DOI : 10.1016/0951-8320(95)00087-9

A. Hussey, B. Lu, and R. Bickford, Performance Enhancement of On-Line Monitoring through Plant Data Classification. Paper presented at the 6th American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Controls and Human Machine Interface Technology, 2009.

A. K. Jardine, D. Lin, D. V. Banjevic, R. Rengaswamy, K. Yin et al., A review on machinery diagnostics and prognostics implementing condition based maintenance. Mechanical Systems and Signal Processing A Review of Process Fault Detection and Diagnosis Part I and III, Computers and Chemical Engineering, vol.20, issue.27, pp.1483-1510, 2003.

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

A. Joentgen, L. Mikenina, R. Weber, and H. J. Zimmermann, Dynamic Fuzzy Data Analysis based on Similarity between Functions. Fuzzy Sets and Systems, pp.81-90, 1999.

S. Kliger, S. Yemini, Y. Yemini, D. Oshie, and S. Stolfo, A Coding Approach to Event Correlation, Proceedings of the 4 th International Symposium on Intelligent Network Management, pp.266-277, 1995.
DOI : 10.1007/978-0-387-34890-2_24

V. A. Kopnov, Optimal degradation processes control by two-level policies, Reliability Engineering & System Safety, vol.66, issue.1, pp.1-11, 1999.
DOI : 10.1016/S0951-8320(99)00006-X

F. Kozin and J. L. Bogdanoff, Probabilistic models of fatigue crack growth: Results and speculations, Nuclear Engineering and Design, vol.115, issue.1, pp.143-171, 1989.
DOI : 10.1016/0029-5493(89)90267-7

C. Lam and R. Yeh, Optimal maintenance-policies for deteriorating systems under various maintenance strategies, IEEE Transactions on Reliability, vol.43, issue.3, pp.423-430, 1994.
DOI : 10.1109/24.326439

M. Laser, Recent safety and environmental l egislation, Transactions of the Institution of Chemical Engineers, vol.78, pp.419-422, 2000.

A. Lehmann, Joint modeling of degradation and failure time data, Journal of Statistical Planning and Inference, vol.139, issue.5, pp.1693-1706, 2006.
DOI : 10.1016/j.jspi.2008.05.027

C. J. Lu and W. Q. Meeker, Using Degradation Measures to Estimate a Time-to-Failure Distribution, Technometrics, vol.70, issue.2, pp.161-173, 1993.
DOI : 10.1080/00401706.1993.10485038

M. Marseguerra, E. Zio, and L. Podofillini, Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation, Reliability Engineering & System Safety, vol.77, issue.2, pp.151-165, 2002.
DOI : 10.1016/S0951-8320(02)00043-1

A. Muller, M. C. Suhner, and B. Iung, Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system, Reliability Engineering & System Safety, vol.93, issue.2, pp.234-253, 2008.
DOI : 10.1016/j.ress.2006.12.004

URL : https://hal.archives-ouvertes.fr/hal-00149602

E. Myotyri, 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

I. Nimmo, Adequately address abnormal situation operations, Chemical Engineering Progress, vol.91, issue.9, pp.36-45, 1995.

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

G. F. Oswald and G. I. Schueller, Realiability of deteriorating structures: Engineering Fracture Mechanics, pp.479-488, 1984.

J. M. Pearl, Probabilistic Reasoning in Intelligent Systems Prognostics and Health Management of Electronics, 1988.

D. J. Pedregal and M. C. Carnero, State Space Models for condition Monitoring: A Case Study: Reliability Engineering and System Safety, pp.171-180, 2006.

L. Peel, October) Data Driven Prognostics using a Kalman Filter Ensemble of Neural Network Models, Paper presented at the International Conference on Prognostics and Health Management, 2008.

R. L. Penha and J. W. Hines, Hybrid System Modeling for Process Diagnostics, Proceedings of the Maintenance and Reliability Conference MARCON, 2002.

Y. Peng, S. Zhang, and R. Pan, BAYESIAN NETWORK REASONING WITH UNCERTAIN EVIDENCES, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.18, issue.05, pp.539-564, 2010.
DOI : 10.1142/S0218488510006696

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

J. W. Provan, Probabilistic Fracture Mechanics and Reliability A Stochastic Model for Wear Prediction through Condition Monitoring, Operational Reliability and Systematic Maintenance, pp.223-243, 1987.

I. Rish, M. Brodie, and S. Ma, Accuracy vs. Efficiency Trade-Offs in Probabilistic Diagnosis, Proceedings of the 18 th National Conference on Artificial Intelligence, 2002.

P. K. Samanta, W. E. Vesely, F. Hsu, and M. Subudly, Degradation modeling with application to ageing and maintenance effectiveness evaluations, NUREG/CR-5612, 1991.

A. Saxena, J. Celaya, E. Balaban, K. Goebel, B. Saha et al., Metrics for evaluating performance of prognostic techniques, 2008 International Conference on Prognostics and Health Management, 2008.
DOI : 10.1109/PHM.2008.4711436

M. Schwabacher, A Survey of Data Driven Prognostics. Paper presented at AIAAA Infotech@Aerospace Conference, 2005.

M. Schwabacher and K. Geobel, A Survey of Artificial Intelligence for Prognostics. Paper presented at the AAAI Fall Symposium, 2007.

J. Z. Sikorska, P. J. Kelly, and J. Mcgrath, Maximizing the Remaining Life of Cranes in Paper presented at the ICOMS Asset Management Conference, 2010.

K. Simola and U. Pulkkinen, Models for non-destructive inspection data, Reliability Engineering & System Safety, vol.60, issue.1, pp.1-12, 1998.
DOI : 10.1016/S0951-8320(97)00087-2

N. Singpurwalla, Survival in Dynamic Environments, Statistical Science, vol.10, issue.1, pp.86-103, 1995.
DOI : 10.1214/ss/1177010132

N. Singpurwalla, Reliability and Risk. A Bayesian Perspective Random fatigue: From data to theory, West Sussex, 1992.

V. Sotiris and M. Pecht, Support Vector Prognostics Analysis of Electronic Products and Systems, Paper presented at the AAAI Conference on Artificial Intelligence, 2007.

V. Sotiris, P. Tse, and M. Pecht, Anomaly Detection Through a Bayesian Support Vector Machine, IEEE Transactions on Reliability, vol.59, issue.2, pp.277-286, 2010.
DOI : 10.1109/TR.2010.2048740

M. Steinder and A. S. Sethi, End-to-End Service Failure Diagnosis Using Belief Networks Instrumentation, Controls and Human-Machine Interface, Proceedings of Network Operations and Management Symposium. IEEE Conference Publications. US Department of Energy, 2002.

G. Vachtsevanos, Performance metrics for fault prognosis of complex systems, Proceedings AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference., pp.341-345, 2003.
DOI : 10.1109/AUTEST.2003.1243597

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

G. Vachtsevanos and P. Wang, Fault prognosis using dynamic wavelet neural networks, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237), pp.857-870, 2001.
DOI : 10.1109/AUTEST.2001.949467

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.1308

P. J. Vlok, J. L. Coetzee, D. Banjevic, A. K. Jardine, and V. Makis, Optimal component replacement decisions using vibration monitoring and the proportional-hazards model, Journal of the Operational Research Society, vol.53, issue.2, pp.193-202, 2002.
DOI : 10.1057/palgrave.jors.2601261

T. Wang, J. Yu, D. Siegel, and J. Lee, A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems, 2008 International Conference on Prognostics and Health Management, 2008.
DOI : 10.1109/PHM.2008.4711421

W. Q. Wang, M. F. Goldnaraghi, and F. Ismail, Prognosis of machine health condition using neuro-fuzzy systems, Mechanical Systems and Signal Processing, pp.813-831, 2004.
DOI : 10.1016/S0888-3270(03)00079-7

X. Wang, G. Yu, and J. Lee, Wavelet Neural Network for Machining Performance Assessment and its Implication to Machinery Prognostic, Paper presented at 5th International Conference on Managing Innovations in Manufacturing (MIM), 2002.

J. Yan, M. Koç, and J. Lee, A prognostic algorithm for machine performance assessment and its application, Production Planning & Control, vol.31, issue.8, pp.796-801, 2004.
DOI : 10.1080/0953728031000057307

J. Yan and J. Lee, A Hybrid Method for On-line Performance Assessment and Life Prediction in Drilling Operations, 2007 IEEE International Conference on Automation and Logistics, 2008.
DOI : 10.1109/ICAL.2007.4338999

R. H. Yeh, State-age-dependent maintenance policies for deteriorating systems with Erlang sojourn time distributions, Reliability Engineering & System Safety, vol.58, issue.1, pp.55-60, 1997.
DOI : 10.1016/S0951-8320(97)00049-5

X. Zhao, M. Fouladirad, C. Berenguer, and L. Bordes, Optimal Periodic Inspection/Replacement Policy of Deteriorating Systems with Explanatory Variables. Paper presented at the European Safety and Reliability Conference, 2008.

V. Zille, A. Despujols, P. Baraldi, G. Rossetti, and E. Zio, A Framework for the Monte Carlo simulation of Degradation and Failure processes in the assessment of maintenance programs performance, 2009.

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

E. Zio and F. Di-maio, 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

. Keywords, Prognostics: prediction of the state of a system, structure or component