[1] Sohn, M.S, Hu .X.Z, Kim J. K and Walker.L, "Impact damage characterization of carbon fiber /epoxy composites with mlti-layer reinforcement", Composites:Part B, vol31, 2000, pp.681-69.
[2] Chen C. and C. Mo: “A method for intelligent fault diagnosis of rotating machinery”, Digital Signal Processing, vol.14, 2004, pp.203–217.
[3] Rafiee, J., M.A. Rafiee, and P.W. Tse: “Application of mother wavelet functions for automatic gear and bearing fault diagnosis”, Expert Systems with Applications, vol.37, 2010, pp.4568–4579.
[4] Goddu, G., B. Li, M.Y. Chow, and J.C. Hung: “Motor Bearing Fault Diagnosis by a Fundamental Frequency Amplitude Based Fuzzy Decision System”, IEEE Industrial Electronics Society Conference, vol.4, 1998, pp.1961-1965.
[5] Zio, E. and G. Gola: “A Neuro-Fuzzy technique for fault diagnosis and its application to rotating machinery”, Reliability Engineering & System Safety, vol.94, no.1, 2009, pp.78–88.
[6] Song, O., T. W. Ha, and L. Librescu, “Dynamics of anisotropic composite cantilevers weakened by multiple transverse open cracks,” Engineering Fracture Mechanics, vol.70, no.1, 2003, pp.105–123.
[7] Just-Agosto, F., D. Serrano, B. Shafiq, and A. Cecchini, “Neural network based nondestructive evaluation of sandwich composites,” Composites B, vol.39, no.1, 2008, pp.217–225.
[8] Perera, R., A. Ruiz, and C.Manzano, “Performance assessment of multicriteria damage identification genetic algorithms,” Computers and Structures, vol.87, no.12, 2009, pp.120–127.
[9] Friswell, M. I., J. E. T. Penny, and S. D. Garvey, “A combined genetic and eigensensitivity algorithmfor the location of damage in structures,” Computers and Structures, vol.69, no5, 1998, pp.547-556.
[10] Fang, X., H. Luo, and J. Tang, “Structural damage detection using neural network with learning rate improvement,” Computers and Structures, vol.83, no.25-26, 2005, pp.2150–2161.
[11] Beena, P. and R. Ganguli, “Structural damage detection usin fuzzy cognitive maps and hebbian learning”, Applied Soft Computing Journal, vol.11, no.1, 2011, pp.1014–1020.
[12] Kuo, H. C. andH. K. Chang, “A new symbiotic evolution-based fuzzy-neural approach to fault diagnosis of marine propulsion systems,” Engineering Applications of Artificial Intelligence, vol.17, no.8, 2004, pp. 919–930.
[13] Wang, W. Q., M. F. Golnaraghi, and F. Ismail, “Prognosis of machine health condition using neuro-fuzzy systems”, Mechanical Systems and Signal Processing, vol.18, no.4, 2004, pp.813–831.
[14] Pawar, P. M. and R. Ganguli, “Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades,” Mechanical Systems and Signal Processing, vol.21, no.5, 2007, pp.2212–2236.
[15] ShariatPanahi, M., N.MoshtaghiYazdani, “An Improved XCSR classifier system for data mining with limited training samples”, Global Journal of Science, Engineering and Technology, Issue.2, 2012, pp.52-57.
[16] Thomson, W. T., “A review of on-line condition monitoringtechniques for three-phase squirrel cage induction motors Past present and future,” IEEE SDEMPED’99, Spain, 1999, pp.3–18.
[17] Nandi, S., H. A. Toliat, X. Li, "Condition monitoring and fault diagnosis of electrical motors-A Review," IEEE Transactions on Energy Conversion, vol.20, no.4, 2005, pp.719-29.
[18] Farrar, C. R., S. Doebling and C. R. Prime, “A summary review of vibration-based damage identification methods” The Shock and Vibration Digest, vol.30, no.2, 1998, pp.91-105.
[19] Thomson, W. T., “A review of on-line condition monitoring techniques for three-phase squirrel cage induction motors— Past present and future,” IEEE SDEMPED’99, Spain, 1999, pp.3–18.
[20] Tandon, N. and A. Choudhury, “A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings,” Journal of Tribology International, vol.32, no.8, 1999, pp.469-480.
[21] Singh, G.K., Sa’ad Ahmed Saleh Al Kazzaz, “Induction machine drive condition monitoring and diagnostic researcha survey,” Journal of electric power research, vol.64, 2003, pp.145-158.
[22] Benbouzid, M. E. H., “A review of induction motors signature analysis as a medium for faults detection,” IEEE Trans. Industrial Electronics, vol.47, no.5, 2000, pp.984-993.
[23] Benbouzid, M. E. H., “What stator current processingbased technique to use for induction motor rotor faults diagnosis?” IEEE Trans. Energy Conversion, vol.18, no.2, 2003, pp.238-244.
[24] Lebold, M.; McClintic, K.; Campbell, R.; Byington, C.; Maynard, K. “Review of Vibration Analysis Methods for Gearbox Diagnostics and Prognostics”, Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA, May 1-4, 2000, p. 623-634.
[25] Yazici, B., G. B. Kliman, “An adaptive statistical timefrequency method for detection of broken bars and bearing faults in motors,” IEEE Trans. On Industry App., vol.35, no.2, 1999, pp.442-52.
[26] Nikolaou, N.G., I.A. Antoniadis, “Rolling element bearing fault diagnosis using wavelet packets,” Journal of NDT & E, vol.35, Issue.3, 2002, pp.197-205.
[27] Prabhakar, S., A. R. Mohanty, A. S Sekhar, “Application of discrete wavelet transform for detection of ball bearing race faults,” Journal of Tribology International, vol.35, no.12, 2002, pp.793-800.
[28] Eren, L., J. Devaney, “Bearing Damage Detection via Wavelet Packet Decomposition of the Stator Current,”IEEE Trans. On Instrumentation and Measurement, vol.53, no.2, 2004, pp.431-6.
[29] Polikar, R, “Ensemble based systems in decision making”, IEEE circuits & systems magazine, vol.6 no.3, 2006, pp.21-45.