P. F. Albrecht, J. C. Appiarius, and D. K. Sharma, Assessment of the reliability of motors in utility applications-Updated, IEEE Transactions on Energy Conversion, vol.1, pp.39-46, 1986.

C. Angeli and A. Chatzinikolaou, On-line fault detection techniques for technical systems: A survey, International journal of computer science & application, vol.1, pp.12-30, 2004.

S. B. Babu, P. B. Vikranth, . Pbaruah_05-]-p, R. B. Baruah, C. Chinnam et al., Fault diagnosis in multi-level inverter system using adaptive back propagation neural networkHMMs for diagnostics and prognostics in machining processesParameter identification and discriminant analysis for jet engine machanical state diagnosisFailure Accommodation in Linear System Through Self ReorganizationAn integrated neural network/expert system approach for fault diagnosis, IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, pp.494-498, 1971.

T. Benkedjouh, K. Medjaher, N. Zerhouni, and S. Rechak, Fault prognostic of bearings by using support vector data description, 2012 IEEE Conference on Prognostics and Health Management, pp.1-7, 2012.
DOI : 10.1109/ICPHM.2012.6299511

URL : https://hal.archives-ouvertes.fr/hal-00805665

U. Boryczkabradley_98, ]. P. Bradley, and O. L. Mangasarian, Finding groups in data: Cluster analysis with ants Feature selection via concave minimization and support vector machines, Machine Learning Proceedings of the Fifteenth International Conference ICML98, pp.61-70, 1998.

. Lee and T. Burgess, What is the prognosis on your maintenance program, Casimir(I)_03], 1995.

. R. Casimir, . E. Boutleux, and G. Clerc, Fault diagnosis in an induction motor by pattern recognition methods Diagnostics for Electric Machines, Casimir(II)_03], pp.294-299, 2003.

. R. Casimir, . E. Boutleux, . G. Clerc, and . Chappuis, Broken bars detection in an induction motor by pattern recognition, 2003 IEEE Bologna Power Tech Conference Proceedings,, 2003.
DOI : 10.1109/PTC.2003.1304328

R. Casimir, Diagnostic des défauts des machines asynchrones par reconnaissance des formesA fast method for determining electrical and mechanical qualities of induction motors using a PC-aided detector, Thèse de l'école Centrale de Lyon, CEGELY, pp.624-629, 1994.

K. Chen, X. Li, F. Wang, T. Wang, C. Wu et al., Bearing fault diagnosis using Wavelet analysisMachine condition prediction based on adaptive neuro fuzzy and high-order particle filtering, Proc. IEEE Int. QRRMSE, 2012, pp.699-702, 2011.

R. B. Chinnam and P. Baruah, A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systemsFuzzy Model Identification based on cluster estimation, Materials and Product Technology Journal of Intelligent Fuzzy Systems, vol.202, pp.166-179267, 1994.

E. Chow and A. S. Willsky, Analytical redundancy and the design of robust failure detection systems Automatic Control, IEEE Transactions on, vol.29, pp.603-614, 1984.

X. Steven and . Ding, Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools, 2008.

. G. Didier, Modélisation et diagnostic de la machine asynchrone en présence de défaillances, Thèse de doctorat, 2004.

J. Doak, An Evaluation of Feature Selection Methods and Their Application to Computer Security, 1992.

M. Dorigo and L. M. Gambardella, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.53-66, 1997.
DOI : 10.1109/4235.585892

B. Dubuisson and M. Masson, A statistical decision rule with incomplete knowledge about classes, Pattern Recognition, vol.26, issue.1, pp.155-165, 1993.
DOI : 10.1016/0031-3203(93)90097-G

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

. P. Flandrin, ]. E. Temps-fréquenceforgy_65, and . Forgy, Traité des nouvelles technologies, HermèsCluster analysis of multivariate data: efficiency vs interpretability of classifications, Biometrics, vol.21, pp.768-769, 1965.

P. M. Frank, On-line fault detection in uncertain nonlinear systems using diagnostic observers: a survey, International Journal of Systems Science, vol.8, issue.12, pp.2129-2154, 1994.
DOI : 10.1109/9.58536

P. M. Frank, Analytical and Qualitative Model-based Fault Diagnosis ??? A Survey and Some New Results, European Journal of Control, vol.2, issue.1, pp.6-28, 1996.
DOI : 10.1016/S0947-3580(96)70024-9

L. Frosini and E. Bassi, Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors, IEEE Transactions on Industrial Electronics, vol.57, issue.1, pp.244-251, 2010.
DOI : 10.1109/TIE.2009.2026770

R. Gouriveau, M. E. Koujok, and N. Zerhouni, Spécification d'un système neuro-flou de prédiction de défaillances à moyen terme, 2007.

A. Helle, Prognostics for Industrial Machinery Availability, Maintenance, Condition Monitoring and Diagnostics, 2006.

J. C. Hoskins, K. M. Kaliyur, and D. M. Himmelblau, Fault diagnosis in complex chemical plants using artificial neural networks, AIChE Journal, vol.37, issue.1, pp.137-141, 1991.
DOI : 10.1002/aic.690370112

T. J. Holroyd, The application of ae in condition monitoringPrediction of marine diesel engine performance under fault conditions, Proceedings of International Conference on Condition Monitoring, pp.1753-1783, 2000.

A. Høyland, M. Rausandhu_64, and ]. M. Hu, System Reliability Theory: Models and Statistical Methods, Application of the Adaline System to Weather Forecasting, 1964.

A. Ibrahim, M. Badaoui, F. Guillet, F. Bonnardotimmo_09-]-f, M. Immovilli et al., A New Bearing Fault Detection Method in Induction Machines Based on Instantaneous Power FactorDetection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signalsBearing Fault Model for Induction Motor with Externally Induced Vibration, IEEE Trans. Ind. Electron IEEE Trans. Ind. Electron IEEE Trans. Ind. Electron, vol.5560, issue.56, pp.4252-42594710, 2008.

R. Isermannjang_93 and ]. J. Jang, Process fault detection based on modeling and estimation methods -a surveyAnfis: adaptive-network-based fuzzy inference system, Jelinek_98] Frederick Jelinek, Statistical Methods for Speech Recognition, pp.387-404665, 1984.

G. H. John, R. Kohavi, and K. Pfleger, Irrelevant Features and the Subset Selection Problem, Machine Learning, pp.121-129, 1994.
DOI : 10.1016/B978-1-55860-335-6.50023-4

C. Juang, C. Lu, C. Lo, C. Wangjain_99, ]. A. Jain et al., Ant colony optimization algorithm for fuzzy controller design and its fpga implementation Industrial ElectronicsData clustering: a reviewMonitoring gear vibrations through motor current signature analysis and wavelet transform, Mechanical Systems and Signal Processing, pp.1453-1462264, 1999.

D. Kim and C. Kim, Forecasting time series with genetic fuzzy predictor ensemble, Fuzzy Systems IEEE Transactions on, vol.5, pp.523-535, 1997.

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

J. V. Kresta, J. F. Macgregor, and T. E. Marlin, Multivariate statistical monitoring of process operating performance, The Canadian Journal of Chemical Engineering, vol.27, issue.1, pp.35-47, 1991.
DOI : 10.1002/cjce.5450690105

A. Krogh, M. Brown, I. Mian, K. Sjolander, D. Haussler et al., Hidden Markov Models in Computational Biology, Journal of Molecular Biology, vol.235, issue.5, pp.25-41, 1993.
DOI : 10.1006/jmbi.1994.1104

K. Van-laerhoven, K. Aidoo, and S. Lowette, Real-time analysis of data from many sensors with neural networks, Proceedings Fifth International Symposium on Wearable Computers, pp.115-123, 2001.
DOI : 10.1109/ISWC.2001.962112

A. Lebaroud and G. Clerc, Classification of Induction Machine Faults by Optimal Time–Frequency Representations, IEEE Transactions on Industrial Electronics, vol.55, issue.12, pp.4290-4298, 2008.
DOI : 10.1109/TIE.2008.2004666

M. Lebold, K. Reichard, and D. Boylan, Utilizing dcom in an open system architecture framework for machinery monitoring and diagnostics, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652), pp.1227-1236, 2003.
DOI : 10.1109/AERO.2003.1235237

J. Liu, D. Djurdjanovic, K. A. Marko, and J. Ni, A divide and conquer approach to anomaly detection, localization and diagnosisA review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings, Mechanical Systems and Signal ProcessingLi_99] N Tandon and A Choudhury, pp.2488-2499469, 1999.

B. Li, M. Y. Chow, Y. Tipsuwan, and J. Hung, Neural-network-based motor rolling bearing fault diagnosis, IEEE Transactions on Industrial Electronics, vol.47, issue.5, pp.1060-1069, 2000.
DOI : 10.1109/41.873214

J. Liu, J. Zhao, L. Li, and Y. Wang, Mechanical fault diagnosis for L-V circuit breakers based on energy spectrum entropy of wavelet packet and Naive Bayesian classifier, 2010 International Conference on Machine Learning and Cybernetics, p.10591064, 2010.
DOI : 10.1109/ICMLC.2010.5580947

K. A. Loparo, M. L. Adams, W. Lin, M. F. Abdel-magied, and N. Afshari, Fault detection and diagnosis of rotating machinery, IEEE Transactions on Industrial Electronics, vol.47, issue.5, pp.1005-1014, 2000.
DOI : 10.1109/41.873208

J. F. Macgregor and T. Kourti, Statistical process control of multivariate processes, Control Engineering Practice, vol.3, issue.3, pp.403-414, 1995.
DOI : 10.1016/0967-0661(95)00014-L

M. C. Mackey, L. Glass, and T. Hiyama, Oscillation and chaos in physiological control systemsFeature extraction and evaluation for Health Assessment and Failure prognosticsPredicting remaining useful life of rotating machinery based artificial neural network, Medjaher_12] Kamal Medjaher, Fatih Camci, and Noureddine Zerhouni First European Conference of the Prognostics and Health Management Society, pp.287-289, 1977.

D. Mba and R. Rao, Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines; bearings, pumps, gearboxes, engines and rotating structures The Shock and vibration digest, pp.3-16, 2006.

D. N-prabhakar-murthy_06, . O. Ondel, . E. Boutleux, and . G. Clerc, Diagnosis and Prognosis of Rolling Element Bearings: Frequency Domain Methods and Hidden Markov Modeling [Ondel(II)_06] O. Ondel, Diagnostic par reconnaissance des formes : application à un ensemble convertisseur-machine asynchrone, Thèse de l'école centrale deA method to detect broken bars in induction machine using pattern recognition techniquesBuilding road-sign classifiers using a trainable similarity measureReview of parity space approaches to fault diagnosis for aerospace systems, Case Studies in Reliability and Maintenance Probability and Statistics, pp.916309-321278, 1994.

R. J. Patton and J. Chen, Observer-based fault detection and isolation: Robustness and applications, Control Engineering Practice, vol.5, issue.5, pp.671-682, 1997.
DOI : 10.1016/S0967-0661(97)00049-X

M. D. Prieto, G. Cirrincione, A. G. Espinosa, J. A. Ortega, and H. Henao, Bearing faults detection by a novel condition monitoring scheme based on statistical-time features and neural networksTurbo machinery condition monitoring and failure prognosis, IEEE Trans. Ind. Electron Sound & vibration, vol.6041, pp.3398-340710, 2007.
DOI : 10.1109/tie.2012.2219838

URL : http://hdl.handle.net/2117/19572

H. Qiu, J. Lee, J. Lin, and G. Yu, Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of Sound and Vibration, vol.289, issue.4-5, pp.1066-1090, 2006.
DOI : 10.1016/j.jsv.2005.03.007

R. Lawrence and . Rabiner, A tutorial on hidden markov models and selected applications in speech recognition, Proceedings of the IEEE, pp.257-286, 1989.

M. J. Roemer and G. J. Kacprzynski, Advanced diagnostics and prognostics for gas turbine engine risk assessment, Aerospace Conference Proceedings, pp.345-353, 2000.
DOI : 10.1109/aero.2000.877909

H. Steven and . Rich, Model-based reasoning in diagnostic expert systems for chemical process plantsA fast training neural network and its updation for incipient fault detection and diagnosis, Reng_00] Raghunathan Rengaswamy and Venkat Venkatasubramanian, pp.111-122, 1987.

I. C. Report, Report of large motor reliability survey of industrial and commercial installation, Part I and Part II, IEEE Transactions on Industry Applications, vol.21, pp.853-872, 1985.

M. Roemer, C. Byington, G. Kacprzynski, and G. Vachtsevanos, An Overview of Selected Prognostic Technologies with Reference to an Integrated PHM Architecture, IEEE Aerospace Conf, 2005.

B. Samanta and C. Nataraj, Prognostics of machine condition using soft computing, Robotics and Computer-Integrated Manufacturing, vol.24, issue.6, pp.816-823, 2008.
DOI : 10.1016/j.rcim.2008.03.011

R. R. Schoen, T. G. Habetler, F. Kamran, and R. G. Bartheld, Motor bearing damage detection using stator current monitoring, IEEE Transactions on Industry Applications, vol.31, issue.6, pp.1274-1279, 1995.
DOI : 10.1109/28.475697

O. R. Seryasat, M. A. Shoorehdeli, F. Honarvar, and A. Rahmani, Multi-fault diagnosis of ball bearing based on features extracted from time-domain and multi-class support vector machine(MSVM), 2010 IEEE International Conference on Systems, Man and Cybernetics, pp.4300-4303, 2010.
DOI : 10.1109/ICSMC.2010.5642390

[. Dash and V. Venkatasubramanian, Challenges in the industrial applications of fault diagnostic systems, Computers & Chemical Engineering, vol.24, issue.2-7, pp.785-791, 2000.
DOI : 10.1016/S0098-1354(00)00374-4

. A. Soualhi, . G. Clerc, . H. Razik, . O. Ondel, T. Stack et al., Detection of induction motor faults by an improved artificial ant clusteringBearing fault detection via autoregressive stator current modelingA structured approach to the selection of condition based maintenance In Factory 2000 -The Technology Exploitation ProcessImproving the reliability of electrical drives through failure prognosis, Proc. IEEE SDEMPED, pp.3446-3451, 1997.

]. T. Takagi_85, M. Takagi, and . Sugeno, Fuzzy identification of systems and its applications to modeling and controlA review of vibration and acoustic measurement methods for the detection of defects in rolling element bearingsAn open standard for web-based condition-based maintenance systems, IEEE Systems Readiness Technology Conference, pp.43-62116, 1985.

D. A. Tobon-mejia, K. Medjaher, N. Zerhouni, and G. Tripot, Hidden Markov Models for failure diagnostic and prognostic, 2011 Prognostics and System Health Managment Confernece, pp.1-8, 2011.
DOI : 10.1109/PHM.2011.5939488

URL : https://hal.archives-ouvertes.fr/hal-00602148

I. Tsoumas, E. Mitronikas, G. Georgoulas, and A. Safacas, A comparative study of induction motor current signature analysis techniques for mechanical faults detection, 2005 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp.1-6, 2005.
DOI : 10.1109/DEMPED.2005.4662545

E. Uchino, T. Yamakawa-ungar, B. A. Powell, S. N. Kamens-rengaswamy, R. et al., Adaptive networks for fault diagnosis and process control [Ven(III)_03] Venkat Venkatasubramanian, Raghunathan Rengaswamy, Surya N. Kavuri, and Kewen YinA review of process fault detection and diagnosis: Part iii: Process history based methodsA review of process fault detection and diagnosis -part ii: Qualitative models and search strategiesA review of process fault detection and diagnosis: Part i: Quantitative model-based methods, Intelligent Hybrid SystemsVen(II)_03] Venkatasubramanian Venkat,Ven(I)_03] Venkat Venkatasubramanian, pp.331-351, 1990.

M. Roemer, A. Hess, G. J. Vachtsevanos, F. L. Lewis, B. Wuvacht_01-]-g et al., Intelligent Fault Diagnosis and Prognosis for Engineering SystemsFault prognosis using dynamic wavelet neural networks, IEEE Systems Readiness Technology Conference, pp.857-870, 2001.

M. Barry, N. B. Wise, . J. Gallagherwalter_04-]-t, H. Walter, . J. Leewang_96-]-w et al., Development of a smart wireless sensor for predicting bearing remaining useful life In Proceedings of the 58th Meeting of the society for machinery failure prevention technologyApplication of wavelets to gearbox vibration signals for fault detectionCommon sense based joint training of human activity recognizersA survey of maintenance policies of deteriorating systems, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp.329-348927, 1996.

J. Wang, L. Shang, S. Chen, and Y. Wang, Application of fuzzy classification by evolutionary neural network in incipient fault detection of power transformer, IEEE International Joint Conference on Neural Networks, pp.2279-2283, 2004.

T. Himmelblau and . Miki, Adaptive signal processing Observer Based Fault Detection in Dynarnic SystemsApplication of Data Mining Technology Based on FRS and SVM for Fault Identification of Power TransformerAnalog implementation of neo-fuzzy neuron and its on-board learning, Computational Intelligence and Applications, pp.1803-1812452, 1985.

T. Yamakawa, E. Uchino, T. Miki, and H. Kusanagi, A neo fuzzy neuron and its applications to system identification and prediction of the system behaviour, Proc. 2nd Int. Conf. Fuzzy Logic Neural Networks, pp.477-483, 1992.

R. C. Yam, P. W. Tse, 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, pp.383-391, 2001.
DOI : 10.1007/s001700170173

A. Yazidi, H. Henao, G. A. Capolino, F. Betin, and F. Filippetti, A Web-Based Remote Laboratory for Monitoring and Diagnosis of AC Electrical Machines, IEEE Transactions on Industrial Electronics, vol.58, issue.10, pp.4950-4959, 2011.
DOI : 10.1109/TIE.2011.2109331

A. Ypma, M. J. David, R. P. Tax, and . Duin, Robust machine fault detection with independent component analysis and support vector data description, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), pp.67-76, 1999.
DOI : 10.1109/NNSP.1999.788124

A. Ypma, E. Ypma, T. Heskeszaidi_11, ]. S. Zaidi, S. Aviyente et al., Prognosis of gear failures in dc starter motors using hidden markov modelsApplication of rough set and support vector machine in fault diagnosis of power electronic circuitA probabilistic fault detection approach: Application to bearing fault detection, Zhan_10] Huaqun Zhan, pp.31-43, 2002.

Y. Zhengyou, P. Tao, L. Jianbao, Y. Huibin, and J. Haiyan, Fault Diagnosis of Rolling Bearing Based on Wavelet Packet Transform and Support Vector Machine, Measuring Technology and Mechatronics Automation, vol.1, pp.650-653, 2009.

. Iso, Condition monitoring and diagnostics of machines -prognostics -Part 1: General guidelines. Int, 2004.