L. H. Chiang, E. Russel, and R. Braatz, ?Fault detection and diagnosis in industrial systems?, 2001.
DOI : 10.1007/978-1-4471-0347-9

K. J. Cios and L. A. Kurgan, Trends in Data Mining and Knowledge Discovery, Knowledge Discovery in Advanced Information Systems, pp.200-202, 2002.
DOI : 10.1007/1-84628-183-0_1

U. M. Fayyad, G. Piatetsky-shapiro, and P. Smyth, ?Knowledge Discovery and Data Mining: Towards a Unifying Framework?, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD96), 1996.

E. Frank, L. Trigg, G. Holmes, and I. H. Witten, ?Technical note: Naive Bayes for regression?, Machine Learning, 2000.

N. G. Gebraeel, M. A. Lawley, . R. Li, and J. K. Ryan, Residual-life distributions from component degradation signals: A Bayesian approach, IIE Transactions, vol.27, issue.6, pp.543-557, 2005.
DOI : 10.1142/S0218539398000091

N. Gebraeel and G. , Sensory-Updated Residual Life Distributions for Components With Exponential Degradation Patterns, IEEE Transactions on Automation Science and Engineering, vol.3, issue.4, 2006.
DOI : 10.1109/TASE.2006.876609

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

A. Heng, S. Zhang, A. C. Tan, and J. Mathew, 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

A. Kelly, ?Maintenance and its management?, Proceedings of the Communication Conference, 1989.

I. Kononenko, Semi-naive bayesian classifier, Proceedings of the 6th European Working Session on Learning, pp.206-219
DOI : 10.1007/BFb0017015

L. Mann, A. Saxena, and G. M. Knapp, Statistical???based or condition???based preventive maintenance?, Journal of Quality in Maintenance Engineering, vol.1, issue.1
DOI : 10.1108/13552519510083156

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.151-65, 2002.
DOI : 10.1016/S0951-8320(02)00043-1

S. Nandi, H. A. Toliyat, and X. Li, Condition Monitoring and Fault Diagnosis of Electrical Motors???A Review, IEEE Transactions on Energy Conversion, vol.20, issue.4, 2005.
DOI : 10.1109/TEC.2005.847955

M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, ?A Bayesian approach to filtering junk e-mail?, AAAI-98 Workshop on Learning for Text Categorization, 1998.

S. Salzberg, ?On comparing classifiers: Pitfalls to avoid and a recommended approach?, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.317-328, 1997.
DOI : 10.1023/A:1009752403260

G. K. Singh and S. A. Saleh-al-kazzazb, ?Induction machine drive condition monitoring and diagnostic research?a survey?, Electric Power Systems Research, pp.145-158, 2003.

L. Torgo and J. Gama, Regression using classification algorithms, Intelligent Data Analysis, vol.1, issue.1-4, pp.275-292, 1997.
DOI : 10.1016/S1088-467X(97)00013-9

J. H. Williams, A. Davies, and I. R. Drake, ?Condition-based maintenance and machine diagnostics?, p.87, 1994.

Y. Yang and G. I. Webb, Discretization for naive-Bayes learning: managing??discretization bias and variance, Machine Learning, vol.41, issue.1, 2009.
DOI : 10.1007/s10994-008-5083-5

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