S. R. Abbas and M. Arif, New Time Series Predictability Metrics for Nearest Neighbor Based Forecasting, 2006 IEEE International Multitopic Conference, 2006.
DOI : 10.1109/INMIC.2006.358144

J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithm, 1981.
DOI : 10.1007/978-1-4757-0450-1

T. Brotherton, G. Jahns, J. Jacobs, and D. Wroblewski, Prognosis of faults in gas turbine engines, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484), pp.163-171, 2000.
DOI : 10.1109/AERO.2000.877892

C. Byington, M. Roemer, G. Kacprzynski, and T. Galie, Prognostic enhancements to diagnostic systems for improved condition-based maintenance, IEEE Aerospace Conf, 2002.

R. B. Chinnam and P. Baruah, A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems, International Journal of Materials and Product Technology, vol.20, issue.1/2/3, pp.166-179, 2004.
DOI : 10.1504/IJMPT.2004.003920

F. X. Diebold and L. Kilian, Measuring predictability: theory and macroeconomic applications, Journal of Applied Econometrics, vol.4, issue.6, pp.657-669, 2001.
DOI : 10.1002/jae.619

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.197.7141

M. Duan, Time Series Predictability, 2002.

M. El-koujok, R. Gouriveau, and N. Zerhouni, Towards a Neuro-Fuzzy System for Time Series Forecasting in Maintenance Applications, IFAC World Congress, 2008.
DOI : 10.3182/20080706-5-KR-1001.02174

URL : https://hal.archives-ouvertes.fr/hal-00298361

M. D. Gauvain, R. Gouriveau, N. Zerhouni, and M. Hessabi, Long term prediction approaches based on connexionist systems - A study for prognostics application, 2011 IEEE Conference on Prognostics and Health Management, 2011.
DOI : 10.1109/ICPHM.2011.6024342

URL : https://hal.archives-ouvertes.fr/hal-00604678

J. F. Gomes-de-freitas, I. M. Macleod, and J. S. Maltz, Neural networks for pneumatic actuator fault detection, Trans. of the South African Institute of Electrical Engineering, vol.90, pp.28-34, 1996.

M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design, 1996.

M. T. Hagan and M. B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, vol.5, issue.6, pp.1989-993, 1994.
DOI : 10.1109/72.329697

A. Heng, S. Zhang, A. Tan, and J. Matwew, Rotating machinery prognostics: State of the art, challenges and opportunities, Mechanical Systems and Signal Processing, vol.23, issue.3, pp.724-739, 2009.
DOI : 10.1016/j.ymssp.2008.06.009

R. Huang, L. Xi, C. R. Liu, H. Qiu, L. et al., Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods, Mechanical Systems and Signal Processing, vol.21, issue.1, pp.193-207, 2007.
DOI : 10.1016/j.ymssp.2005.11.008

J. S. Jang and C. T. Sun, Neuro-fuzzy modeling and control, Proceedings of the IEEE, vol.83, issue.3, pp.378-406, 1995.
DOI : 10.1109/5.364486

A. K. Jardine, D. Lin, and D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Systems and Sig. Processing, pp.1483-1510, 2006.
DOI : 10.1016/j.ymssp.2005.09.012

M. Kaboudan, A measure of time series' predictability using genetic programming applied to stock returns, Journal of Forecasting, vol.21, issue.5, pp.345-357, 1999.
DOI : 10.1002/(SICI)1099-131X(199909)18:5<345::AID-FOR744>3.0.CO;2-7

M. Lebold and M. Thurston, Open standards for condition-based maintenance and prognostics systems, 5th Annual Maintenance and Reliability Conf, 2001.

C. Li and K. H. Cheng, Recurrent neuron-fuzzy hybrid-learning approach to accurate system modeling. Fuzzy Sets and Systems, pp.194-212, 2007.

E. Ramasso and R. Gouriveau, Prognostics in switching systems: Evidential markovian classification of real-time neuro-fuzzy predictions, 2010 Prognostics and System Health Management Conference, 2010.
DOI : 10.1109/PHM.2010.5413442

URL : https://hal.archives-ouvertes.fr/hal-00459310

A. Saxena, K. Goebel, D. Simon, E. , and N. , Damage propagation modeling for aircraft engine runto-failure simulation, Inter. Conf. on Prognostics and Health Management, 2008.
DOI : 10.1109/phm.2008.4711414

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.1192

H. N. Teodorescu and L. I. Fira, Analysis of the predictability of time series obtained from genomic sequences by using several predictors, Jour. of Intelligent and Fuzzy Systems, vol.19, pp.51-63, 2008.

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

W. Q. Wang, An adaptive predictor for dynamic system forecasting, Mech. Systems and Sig. Processing, pp.809-823, 2007.
DOI : 10.1016/j.ymssp.2005.12.008

W. Q. Wang, F. Ismail, and M. F. Goldnaraghi, A Neuro-Fuzzy Approach to Gear System Monitoring, IEEE Transactions on Fuzzy Systems, vol.12, issue.5, pp.710-723, 2004.
DOI : 10.1109/TFUZZ.2004.834807

W. Q. Wang and G. Vachtsevanos, Fault prognostics using dynamic wavelet neural networks, AI EDAM, vol.15, issue.4, pp.349-365, 2001.
DOI : 10.1017/S0890060401154089

W. Wang, P. V. Gelder, and J. K. Vrijling, Measuring predictability of daily streamflow processes based on univariate time series model, iEMSs, pp.3474-3478, 2008.

R. C. Yam, P. W. Tse, L. Li, T. , and P. , Intelligent Predictive Decision Support System for Condition-Based Maintenance, The International Journal of Advanced Manufacturing Technology, vol.17, issue.5, pp.383-391, 2001.
DOI : 10.1007/s001700170173