D. An, J. H. Choi, K. , and N. H. , Prognostics 101: A tutorial for particle filter-based prognostics algorithm using matlab, Reliability Engineering & System Safety, vol.115, pp.161-169, 2013.
DOI : 10.1016/j.ress.2013.02.019

M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. Signal Processing, IEEE Transactions on, vol.50, issue.2, pp.174-188, 2002.
DOI : 10.1109/78.978374
URL : http://www.cs.ubc.ca/~murphyk/Software/Kalman/ParticleFilterTutorial.pdf

N. J. Gordon, D. J. Salmond, and A. F. Smith, Novel approach to nonlinear/non-gaussian bayesian state estimation, Radar and Signal Processing, vol.140, pp.107-113, 1993.
DOI : 10.1049/ip-f-2.1993.0015

A. L. Hartzell, M. G. Da-silva, and H. R. Shea, MEMS reliability. EPFL-BOOK-154162, 2011.

A. Heng, S. Zhang, A. C. Tan, M. , and J. , Rotating machinery prognostics: state of the art, challenges and opportunities. Mechanical Systems and Signal Processing, vol.23, pp.724-739, 2009.

Y. Huang, A. S. Vasan, R. Doraiswami, M. Osterman, and M. Pecht, MEMS reliability review. Device and Materials Reliability, vol.12, pp.482-493, 2012.
DOI : 10.1109/tdmr.2012.2191291
URL : http://www.pgembeddedsystems.com/securelogin/upload/project/IEEE/1/pg2012-2013e17/IEEE010.pdf

A. K. Jardine, D. Lin, and D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, vol.20, pp.1483-1510, 2006.
DOI : 10.1016/j.ymssp.2005.09.012

K. Javed, R. Gouriveau, N. Zerhouni, and P. Nectoux, Enabling health monitoring approach based on vibration data for accurate prognostics. Industrial Electronics, IEEE Transactions on, vol.62, issue.1, pp.647-656, 2015.
DOI : 10.1109/tie.2014.2327917
URL : https://hal.archives-ouvertes.fr/hal-01142167

M. Jouin, R. Gouriveau, D. Hissel, M. C. Péra, and N. Zerhouni, Prognostics of PEM fuel cell in a particle filtering framework, International Journal of Hydrogen Energy, vol.39, issue.1, pp.481-494, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00903643

M. Lebold and M. Thurston, Open standards for condition-based maintenance and prognostic systems, Maintenance and Reliability Conference (MARCON), pp.6-9, 2001.

Y. Li and Z. Jiang, An overview of reliability and failure mode analysis of microelectromechanical systems (MEMS), Handbook of performability engineering, pp.953-966, 2008.

K. Medjaher, H. Skima, and N. Zerhouni, Condition assessment and fault prognostics of microelectromechanical systems, Microelectronics Reliability, vol.54, pp.143-151, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00929738

K. Medjaher and N. Zerhouni, Hybrid prognostic method applied to mechatronic systems, The International Journal of Advanced Manufacturing Technology, vol.69, issue.1-4, pp.823-834, 2013.
DOI : 10.1007/s00170-013-5064-0
URL : https://hal.archives-ouvertes.fr/hal-00867575

W. Merlijn-van-spengen, MEMS reliability from a failure mechanisms perspective. Microelectronics Reliability, vol.43, pp.1049-1060, 2003.

A. Mosallam, K. Medjaher, and N. Zerhouni, Data-driven prognostic method based on bayesian approaches for direct remaining useful life prediction, Journal of Intelligent Manufacturing, pp.1-12, 2014.
DOI : 10.1007/s10845-014-0933-4
URL : https://hal.archives-ouvertes.fr/hal-01025442

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

Y. Peng, M. Dong, and M. J. Zuo, Current status of machine prognostics in condition-based maintenance: a review, The International Journal of Advanced Manufacturing Technology, vol.50, issue.1-4, pp.297-313, 2010.

B. Saha and K. Goebel, Model adaptation for prognostics in a particle filtering framework, International Journal of Prognostics and Health Management, vol.2, p.61, 2011.

D. A. Tobon-mejia, K. Medjaher, N. Zerhouni, and G. Tripot, A data-driven failure prognostics method based on mixture of gaussians hidden markov models. Reliability, IEEE Transactions on, vol.61, issue.2, pp.491-503, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00737585

S. Yin and X. Zhu, Intelligent particle filter and its application on fault detection of nonlinear system. Industrial Electronics, IEEE Transactions on, vol.62, issue.6, pp.3852-3861, 2015.