, A comprehensive analysis of the current status in low voltage induction motor diagnosis, Proc. of the ICEM'2000, pp.595-602, 2000.
, Advances in Diagnostic Techniques for Induction Machines, IEEE Transactions on Industrial Electronics, vol.55, issue.12, pp.4109-4126, 2008.
DOI : 10.1109/TIE.2008.2007527
, Wireless system for monitoring wind turbine generators, Proc. of the SPEEDAM, pp.815-820, 2004.
, Analytical approach of the frequency response for the wound rotor induction machine for diagnosis purpose, 2005 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp.21-26, 2005.
DOI : 10.1109/DEMPED.2005.4662535
, Experimental inter-turn short circuit fault characterization of wound rotor induction machines, 2010 IEEE International Symposium on Industrial Electronics, pp.2615-2820, 2010.
DOI : 10.1109/ISIE.2010.5637971
URL : https://hal.archives-ouvertes.fr/hal-00617992
, Doubly Fed Induction Machines Diagnosis Based on Signature Analysis of Rotor Modulating Signals, IEEE Transactions on Industry Applications, vol.44, issue.6, pp.1711-1721, 2008.
DOI : 10.1109/TIA.2008.2006322
, Neural Networks for Pattern Recognition, 1995.
, Neural networks aided on-line diagnostics of induction motor rotor faults, Conf. Rec.1993 IEEE IAS Annual Meeting, pp.316-323, 1993.
, A neural approach for the fault diagnostics in induction machines, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02, pp.3372-3376, 2002.
DOI : 10.1109/IECON.2002.1182939
, Self-commissioning training algorithms for neural networks with applications to electric machine fault diagnostics, IEEE Trans. on Power Electronics, pp.1089-1095, 2002.
DOI : 10.1109/TPEL.2002.805611
, Neural network for digital applications, IEEE International Symposium on Circuits and Systems, pp.2189-2192, 1989.
DOI : 10.1109/ISCAS.1989.100811
, A full-parallel digital implementation for pre-trained NNs, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, pp.49-54, 2000.
DOI : 10.1109/IJCNN.2000.857873
, Digitizing artificial neural networks, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94), pp.3986-3689, 1994.
DOI : 10.1109/ICNN.1994.374850
, Detection and interactive isolation of faults in steam turbines to support maintenance decisions, Simulation Modelling Practice and Theory, vol.16, issue.10, pp.1689-1703, 2008.
DOI : 10.1016/j.simpat.2008.08.013
, Development of an Intelligent Diagnostic System for Induction Machine Health Monitoring, IEEE Systems Journal, vol.2, issue.2, pp.273-288, 2008.
DOI : 10.1109/JSYST.2008.924129
, Improving Elman Network using genetic algorithm for bearing failure diagnosis of induction motor, 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp.1-6, 2009.
DOI : 10.1109/DEMPED.2009.5292794
, Back propagation neural network for classification of induction machine faults, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, pp.525-528, 2011.
DOI : 10.1109/DEMPED.2011.6063673
URL : https://hal.archives-ouvertes.fr/hal-00734092
, Static-, Dynamic-, and Mixed-Eccentricity Fault Diagnoses in Permanent-Magnet Synchronous Motors, IEEE Transactions on Industrial Electronics, vol.56, issue.11, pp.4727-4739, 2009.
DOI : 10.1109/TIE.2009.2029577
, Development and implementation of an ANN-based fault diagnosis scheme for generator winding protection, IEEE Transactions on Power Delivery, vol.16, issue.2, pp.208-214, 2001.
DOI : 10.1109/61.915484
, Identification of induction motor at standstill using artificial neural network, IECON 2010, 36th Annual Conference on IEEE Industrial Electronics Society, pp.2908-2913, 2010.
DOI : 10.1109/IECON.2010.5674931
, Neural MPPT Control of Wind Generators With Induction Machines Without Speed Sensors, IEEE Transactions on Industrial Electronics, vol.58, issue.1, pp.37-47, 2011.
DOI : 10.1109/TIE.2010.2043043
, An adaptive precise one-end power transmission line fault locating algorithm based on multilayer complex adaptive artificial neural networks, 2009 IEEE International Conference on Industrial Technology, pp.1-6, 2009.
DOI : 10.1109/ICIT.2009.4939590
, Alarm processing and fault diagnosis in power systems using Artificial Neural Networks and Genetic Algorithms, 2010 IEEE International Conference on Industrial Technology, pp.14-17, 2010.
DOI : 10.1109/ICIT.2010.5472574
, General Regression Neural Networks as rotor fault detectors of the induction motor, 2010 IEEE International Conference on Industrial Technology, pp.14-17, 2010.
DOI : 10.1109/ICIT.2010.5472618
, Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor, IEEE Transactions on Industrial Electronics, vol.58, issue.5, pp.1555-1563, 2011.
DOI : 10.1109/TIE.2010.2053337
, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research Samuel Toma was born in Alexandria, Egypt in 1987 In the same year he received a MSc in computer science from University of Corsica Currently he is preparing a PhD, 2010 he received the BSc in communication and network engineering from the French Univerity in Egypt (UFE) the University of Corsica. His main research interests are based on the Discrete Event Systems (DEVS) and their applications in the electrical domain using artificial neural networks
, Ecole Supérieure d'Ingénieurs de Nice Sophia Antipolis He is SCS (Society for Modeling and Simulation International) member since 2001 and he is also faculty of the SPE ( " Sciences pour l'environnement " ) Laboratory at the University of Corsica. His main research concerns the modeling and simulation of complex systems like digital and electrical equipments by using distributed or sequential discrete event approaches. His research goal is to propose a new approach for the modeling and the concurrent simulation of behavioral faults for complex systems using a discrete event approach. He is founder member of the DEVSimPy open source project, PMID: 10815714. Gérard-André Capolino (A'77, M'82, SM'89, pp.717-727, 2000.
, Increasing the accuracy of neural network classification using refined training data, Environmental Modelling & Software, vol.24, issue.7, pp.850-858, 2009.
DOI : 10.1016/j.envsoft.2008.11.012
, Artificial neural networks, The Lancet, vol.350, issue.9085, pp.3-11, 2000.
DOI : 10.1016/S0140-6736(05)63820-2
, The application of artificial neural networks to the analysis of remotely sensed data, International Journal of Remote Sensing, vol.66, issue.3, pp.617-663, 2008.
DOI : 10.1080/01431169408954326
, Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems, 2005.
DOI : 10.1007/b97585
, DEVSimPy: A Collaborative Python Software for Modeling and Simulation of DEVS Systems, 2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp.170-175, 2011.
DOI : 10.1109/WETICE.2011.31
URL : https://hal.archives-ouvertes.fr/hal-00603466
, DEVSimPy software, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01083091
, PythonDEVS : A modeling and simulation package for classical hierarchal DEVS, 2001.
, Wound-rotor induction generator short-circuit fault classification using a new neural network based on digital data, 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives, pp.638-644, 2011.
DOI : 10.1109/DEMPED.2011.6063691
URL : https://hal.archives-ouvertes.fr/hal-00603745