A Hybrid TSA-Fuzzy Logic Approach to Detect Induction Motor Rotor Faults

By abdennabi khiam, Nabil Ngote, Mohammed Ouassaid


This paper treats the broken rotor bar fault detection and diagnosis in induction motors. Indeed, the broken rotor bar fault do not have an initial effect can lead an induction motor to fail, there can be serious secondary effects. In this context, a new fault detection and diagnosis approach, namely the TSA-Fuzzy are presented. This technique uses the residual current, obtained by the TSA method. In fact, this current enables to detect faults that cannot be detected by analyzing the stator current, especially in the low load motor case. The RMS of residual current and the load will be used as inputs for the fuzzy logic bloc, where the decision about the state of the rotor is made. The results show the reliability and the efficiency of the proposed approach

Full Text:



Ravi C. Bhavsar, Various Techniques for Condition Monitoring of Three Phase Induction Motor- A Review, Int. J. Eng. Invent., vol. 3, no. 4, pp. 22–26, 2013.

M. R. Mehrjou, N. Mariun, and M. H. Marhaban, Rotor fault condition monitoring techniques for squirrel-cage induction machine — A review, Mech. Syst. Signal Process., vol. 25, no. 8, pp. 2827–2848, 2011.

S. M. Lu, A review of high-efficiency motors: Specification, policy, and technology, Renew. Sustain. Energy Rev., vol. 59, pp. 1–12, 2016.

W. T. Thomson and M. Fenger, Current signature analysis to detect induction motor faults, IEEE Ind. Appl. Mag., vol. 7, no. 4, pp. 26–34, 2001.

W.Laala, S.Guedidi, S.Zouzou Novel approach diagnosis and detection of brokn rotor bar in induction motor at low slip using fuzzy logic, IEEE, pp. 511–516, 2011.

S. Bindu and V. V. Thomas, Diagnoses of internal faults of three phase squirrel cage induction motor - A review, Proc. 2014 Int. Conf. Adv. Energy Convers. Technol. - Intell. Energy Manag. Technol. Challenges, ICAECT 2014, pp. 48–54, 2014.

M. J. Picazo-Ródenas, J. Antonino-Daviu, V. Climente-Alarcon, R. Royo-Pastor, and A. Mota-Villar, Combination of noninvasive approaches for general assessment of induction motors, IEEE Trans. Ind. Appl., vol. 51, no. 3, pp. 2172–2180, 2015.

M. Eftekhari, M. Moallem, S. Sadri, and A. Shojaei, Review of induction motor testing and monitoring methods for inter-turn stator winding faults, 2013 21st Iran. Conf. Electr. Eng. ICEE 2013, 2013.

P. A. Delgado-Arredondo, D. Morinigo-Sotelo, R. A. Osornio-Rios, J. G. Avina-Cervantes, H. Rostro-Gonzalez, and R. de J. Romero-Troncoso, Methodology for fault detection in induction motors via sound and vibration signals, Mech. Syst. Signal Process., pp. 1–22, 2016.

S. Guedidi, S. E. Zouzou, W. Laala, M. Sahraoui, and K. Yahia, Broken bar fault diagnosis of induction motors using MCSA and neural network, SDEMPED 2011 - 8th IEEE Symp. Diagnostics Electr. Mach. Power Electron. Drives, no. 1, pp. 632–637, 2011.

B. Bessam, A. Menacer, M. Boumehraz, and H. Cherif, Detection of broken rotor bar faults in induction motor at low load using neural network, ISA Trans., vol. 64, pp. 241–246, 2016.

G. Bosque, I. Del Campo, and J. Echanobe, Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades, Eng. Appl. Artif. Intell., vol. 32, no. 1, pp. 283–331, 2014.

Y. Maouche, M. E. K. Oumaamar, M. Boucherma, and A. Khezzar, Instantaneous power spectrum analysis for broken bar fault detection in inverter-fed six-phase squirrel cage induction motor, Int. J. Electr. Power Energy Syst., vol. 62, pp. 110–117, 2014.

S. Bachir, S. Tnani, T. Poinot, and J. C. Trigeassou, The synchronous (time domain) average revisited, Mechanical Systems and Signal Processing, Vol. 25, pp. 1087–1102, 2011.

S. Braun, The synchronous (time domain) average revisited, Mech. Syst. Signal Process., vol. 25, no. 4, pp. 1087–1102, 2011.

W. Wu, J. Lin, S. Han, and X. Ding, Time domain averaging based on fractional delay filter, Mech. Syst. Signal Process., vol. 23, no. 5, pp.

–1457, 2009.

S. E. Zouzou, W. Laala, S. Guedidi, and M. Sahraoui, A Fuzzy Logic

Approach for the Diagnosis of Rotor Faults in Squirrel Cage Induction

Motors, In Computer and Electrical Engineering, 2009. ICCEE’09. Second International Conference on IEEE, Vol. 2, pp. 173-177.

N. Ngote, S. Guedira, and M. Cherkaoui, A new approach to diagnose induction motor defects based on the combination of the TSA method and MCSA technique, WSEAS Trans. Signal Process., vol. 8, no. 3, pp. 77–86, 2012.

Y. Gritli, L. Zarri, C. Rossi, F. Filippetti, G. A. Capolino, and D. Casadei, Advanced diagnosis of electrical faults in wound-rotor induction machines, IEEE Trans. Ind. Electron., vol. 60, no. 9, pp. 4012–4024, 2013.

H. F. Azgomi and J. Poshtan, Induction motor stator fault detection via fuzzy logic, 2013 21st Iran. Conf. Electr. Eng. ICEE 2013, pp. 5–9, 2013..

M. Akar and I. Cankaya, Broken rotor bar fault detection in inverter-fed squirrel cage induction motors using stator current analysis and fuzzy logic, Turkish J. Electr. Eng. Comput. Sci., vol. 20, no. SUPPL.1, pp. 1077–1089, 2012.

International Journal of Information Science and Technology (iJIST) – ISSN: 2550-5114