References
[1]. Abellan-Nebot, J. V., & Subirón, F. R. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology, 47(1-4), 237-257.
[2]. Ahamed, S. K., Karmakar, S., Mitra, M., & Sengupta, S. (2010). Diagnosis of induction motor faults due to broken rotor bar and rotor mass unbalance through discrete wavelet transform of starting current at no-load. Journal of Electrical Systems, 6(3), 442-456.
[3]. Dias, C. G., & Pereira, F. H. (2018). Broken rotor bars detection in induction motors running at very low slip using a hall effect sensor. IEEE Sensors Journal, 18(11), 4602- 4613.
[4]. Gaeid, K. S., Ping, H. W., Khalid, M., & Salih, A. L. (2011). Fault diagnosis of induction motor using MCSA and FFT. Electrical and Electronic Engineering, 1(2), 85- 92.
[5]. Guedidi, S., Zouzou, S. E., Laala, W., Yahia, K., & Sahraoui, M. (2013). Induction motors broken rotor bars detection using MCSA and neural network: Experimental research. International Journal of System Assurance Engineering and Management, 4(2), 173-181.
[6]. Guldemir, H., & Bradley, K. J. (2003). An improved approach to the prediction of line current spectrum in induction machines. Electrical Engineering, 86(1), 17-24.
[7]. Hassan, O. E., Amer, M., Abdelsalam, A. K., & Williams, B. W. (2018). Induction motor broken rotor bar fault detection techniques based on fault signature analysis–a review. IET Electric Power Applications, 12(7), 895-907.
[8]. Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483-1510.
[9]. Kurek, J., & Osowski, S. (2010). Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor. Neural Computing and Applications, 19(4), 557-564.
[10]. Mehala, N., & Dahiya, R. (2008, December). A comparative study of FFT, STFT, and wavelet techniques for induction machine fault diagnostic analysis. In Proceedings of the 7th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (Vol.2931).
[11]. Mehala, N., & Dahiya, R. (2009). Condition monitoring methods, failure identification and analysis for Induction machines. International Journal of Circuits, Systems and Signal Processing, 3(1), 10-17.
[12]. Prieto, M. D., Cirrincione, G., Espinosa, A. G., Ortega, J. A., & Henao, H. (2013). Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Transactions on Industrial Electronics, 60(8), 3398-3407.
[13]. Rai, V. K., & Mohanty, A. R. (2007). Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mechanical Systems and Signal Processing, 21(6), 2607-2615.
[14]. Rodríguez, P. V. J., Negrea, M., & Arkkio, A. (2008). A simplified scheme for induction motor condition monitoring. Mechanical Systems and Signal Processing, 22(5), 1216-1236.
[15]. Stockwell, R. G., Mansinha, L., & Lowe, R. P. (1996). Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 44(4), 998-1001.
[16]. Trachi, Y., Elbouchikhi, E., Choqueuse, V., & Benbouzid, M. (2015, November). Stator current analysis by subspace methods for fault detection in induction machines. In Industrial Electronics Society, IECON 2015- 41st Annual Conference of the IEEE (pp.3479-3484). IEEE.
[17]. Wang, D., Peter, W. T., & Tsui, K. L. (2013). An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 35(1-2), 176-199.
[18]. Wang, J., Gao, R. X., & Yan, R. (2011). Broken-rotor-bar diagnosis for induction motors. In Journal of Physics: Conference Series (Vol. 305, No. 1, p. 012026). IOP Publishing.
[19]. Widodo, A., Kim, E. Y., Son, J. D., Yang, B. S., Tan, A. C., Gu, D. S., ... & Mathew, J. (2009). Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, 36(3), 7252-7261.
