Skip to main content

A Review to Diagnose Faults Related to Three-Phase Industrial Induction Motors


The induction motor is one of the essential components of the industry. In industrial applications, the controllability, protection, and reliability of induction motors are of major concern to reserve energy. Thus, condition monitoring of an induction motor is critical for improving the unit's reliability and, as a result, reducing downtime, labor, reducing energy wastage. Induction motor stator winding and bearing failures account for 37% and 41% of failures, respectively. As the sophisticated controls rely on parameters of the motor, while the protection and reliability depend on continuous and accurate monitoring of the health of the motor. This paper covers the state of the art of parameter estimation and condition monitoring of induction motors in order to help out the industry to minimize energy wastage. In order to assist the exact operation of an induction motor, initially, the fundamentals, structure, and model of an induction motor are explained. Further, this paper covers fault diagnoses that are capable of finding the symptoms of motor failure through the state of the art of parameter estimation. In addition, the medium for the root cause of an induction motor failure is described. Finally, the paper is concluded with what has been done already, the knowledge gap, and the potential of future research.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Data Availability

The data that support the findings of this study are available from the corresponding author, Muhammad Aman Sheikh, upon reasonable request.


  1. S. Rahman, and A.A.B.Z. Abidin (2016) A Review on Induction Motor Speed Control Methods. Int J Core Eng. Manag. 3(5)

  2. N. Rajeswaran, M.L. Swarupa, T.S. Rao, K. Chetaswi, Hybrid artificial intelligence based fault diagnosis of svpwm voltage source inverters for induction motor. Mater. Today Proc. 5(1), 565–571 (2018)

    Google Scholar 

  3. D. Ramya, R. Basha and M. Bharathi (2021) Fault diagnosis of induction motor drive using motor current signature analysis.

  4. T. Amanuel et al., Comparative analysis of signal processing techniques for fault detection in three phase induction motor. J. Electron. 3(01), 61–76 (2021)

    Google Scholar 

  5. M.A.Sheikh et al. An Unsupervised Automated Method to Diagnose Industrial Motors Faults. in 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). 2018. IEEE.

  6. O. AlShorman et al., Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study. Adv. Mech. Eng. 13(2), 1687814021996915 (2021)

    Google Scholar 

  7. N.Mishra, K. Roy, and P. Rautela. Investigation of motor faults in NPP using in service motor monitoring system. in Power Electronics, Intelligent Control and Energy Systems (ICPEICES), IEEE International Conference on. 2016. IEEE.

  8. R. Patole, and M. Bhagwat. Modelling of healthy and faulty three phase induction motor in LabVIEW. in Inventive Computation Technologies (ICICT), International Conference on. 2016. IEEE.


  10. O. Yaman, An automated faults classification method based on binary pattern and neighborhood component analysis using induction motor. Measurement. 168, 108323 (2021)

    Google Scholar 

  11. A. Boglietti, A. Cavagnino, A. Krings, New magnetic materials for electrical machines and power converters. IEEE Trans. Ind. Electron. 64(3), 2402–2404 (2017)

    Google Scholar 

  12. M.A. Sheikh et al., Unsupervised on-line method to diagnose unbalanced voltage in three-phase induction motor. Neural Comput. Appl. 30(12), 3877–3892 (2018)

    Google Scholar 

  13. X.Wen, (2011) A hybrid intelligent technique for induction motor condition monitoring. University of Portsmouth.

  14. M.R. Mehrjou et al., Rotor fault condition monitoring techniques for squirrel-cage induction machine-a review. Mech. Syst. Signal Process. 25(8), 2827–2848 (2011)

    Google Scholar 

  15. K. Boughrara et al., Analytical analysis of cage rotor induction motors in healthy, defective, and broken bars conditions. IEEE Trans. Magn. 51(2), 1–17 (2015)

    Google Scholar 

  16. Y. Gritli et al., Advanced diagnosis of electrical faults in wound-rotor induction machines. IEEE Trans. Ind. Electron. 60(9), 4012–4024 (2013)

    Google Scholar 

  17. S.K. Gundewar, P.V. Kane, Condition monitoring and fault diagnosis of induction motor. J. Vib.Eng.Technol. 9(4), 643–674 (2021)

    Google Scholar 

  18. O.C. Soygenç, A. Tap, and L.T. Ergene. Efficiency analysis in three phase squirrel cage induction motor. in Electrical, Electronics and Biomedical Engineering (ELECO), 2016 National Conference on. 2016. IEEE.


  20. J.D. Widmer, R. Martin, M. Kimiabeigi, Electric vehicle traction motors without rare earth magnets. Sustain. Mater. Technol. 3, 7–13 (2015)

    Google Scholar 


  22. A. Trzynadlowski, The field orientation principle in control of induction motors. (Springer Science & Business Media, Newyork, 2013)

    Google Scholar 

  23. A.E. Fitzgerald et al., Electric machinery. (McGraw-Hill, New York, 2003)

    Google Scholar 

  24. X. Xue, Health Monitoring of Drive Connected Three-Phase Induction Motors-From Wired Towards Wireless Sensor Networks. (University of California, Riverside, 2009)

    Google Scholar 

  25. M. Ojaghi, M. Sabouri, J. Faiz, Diagnosis methods for stator winding faults in three-phase squirrel-cage induction motors. Int. Trans. Electrical Energy Syst. 24(6), 891–912 (2014)

    Google Scholar 

  26. K. Yong-Hwa et al., High-resolution parameter estimation method to identify broken rotor bar faults in induction motors. IEEE Trans. Ind. Electron. 60(9), 4103–4117 (2013)

    Google Scholar 

  27. H. Divdel, M.H. Moghaddam, G. Alipour, A new diagnosis of severity broken rotor bar fault based modeling and image processing system. J.Curr.Res. Sci. 1, 771 (2016)

    Google Scholar 

  28. C.-C. Kuo et al., Implementation of a motor diagnosis system for rotor failure using genetic algorithm and fuzzy classification. Appl. Sci. 7(1), 31 (2016)

    Google Scholar 

  29. M.A. Sheikh et al., An intelligent automated method to diagnose and segregate induction motor faults. J. Electrical Syst. 13(2), 241–254 (2017)

    Google Scholar 

  30. T.A. Garcia-Calva et al., Time-frequency analysis based on minimum-norm spectral estimation to detect induction motor faults. Energies. 13(16), 4102 (2020)

    Google Scholar 

  31. J. Zarei, M.A. Tajeddini, H.R. Karimi, Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics. 24(2), 151–157 (2014)

    Google Scholar 

  32. D.D. Sabin, A.R. Dettloff, and P. Golden. (2016) Automatic subtransmission fault location system using power quality monitors. in Transmission and Distribution Conference and Exposition (T&D), IEEE/PES. 2016. IEEE.

  33. S. Karmakar et al., Induction motor fault diagnosis: approach through current signature analysis. (Springer, 2016)

    Google Scholar 

  34. K. Pandey, P. Zope, and S. Suralkar, (2012) Review on fault diagnosis in three-phase induction MEDHA–2012, Proceedings published by International Journal of Computer Applications (IJCA).

  35. S. Bhattacharyya et al., Induction motor fault diagnosis by motor current signature analysis and neural network techniques. J. Adv. Comput. Commun. Technol. 3(1), 12–18 (2015)

    Google Scholar 

  36. M.A. Sheikh et al., An analytical and experimental approach to diagnose unbalanced voltage supply. Arab. J. Sci. Eng. 43(6), 2735–2746 (2018)

    Google Scholar 

  37. A. Adouni, A.J. Marques Cardoso, Thermal analysis of low-power three-phase induction motors operating under voltage unbalance and inter-turn short circuit faults. Machines. 9(1), 2 (2020).

    Article  Google Scholar 

  38. P. Tavner, L. Ran, J. Penman, H. Sedding, Condition monitoring of rotating electrical machines. (Institution of Engineering and Technology, 2008)

    Google Scholar 


  40. S. Grubic et al., A Survey on testing and monitoring methods for stator insulation systems of low-voltage induction machines focusing on turn insulation problems. IEEE Trans. Ind. Electron. 55(12), 4127–4136 (2008)

    Google Scholar 

  41. M.Eftekhari, et al. Review of induction motor testing and monitoring methods for inter-turn stator winding faults. in 2013 21st Iranian Conference on Electrical Engineering (ICEE). 2013. IEEE.

  42. A. Soualhi, G. Clerc, H. Razik, Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Trans. Industr. Electron. 60(9), 4053–4062 (2013)

    Google Scholar 

  43. M.A. Sheikh, N.M. Nor, and T. Ibrahim, (2016) A new method for detection of unbalance voltage supply in three phase induction motor. Jurnal Teknologi, 78(5–8).

  44. M.A. Sheikh, et al.(2019), Invasive methods to diagnose stator winding and bearing defects of an induction motors, in Advanced Condition Monitoring and Fault Diagnosis of Electric Machines. IGI Global. p. 122–130.

  45. M.A. Sheikh, et al. (2017) Non-invasive methods for condition monitoring and electrical fault diagnosis of induction motors. Fault Diagnosis and Detection, p. 263.

  46. M. Irfan, A. Alwadie, N. Saad, & M. A. Sheikh, (2019) Analysis of bearing faulty cage using non-intrusive condition monitoring techniques. in International Conference on Renewable Energies and Power Quality (ICREPQ’19)

  47. S. Ganesan et al., Intelligent starting current-based fault identification of an induction motor operating under various power quality issues. Energies. 14(2), 304 (2021)

    Google Scholar 

  48. J. Tang et al., Modeling and evaluation of stator and rotor faults for induction motors. Energies. 13(1), 1–1 (2019)

    Google Scholar 

  49. M. Skowron et al., Convolutional neural network-based stator current data-driven incipient stator fault diagnosis of inverter-fed induction motor. Energies. 13(6), 1475 (2020)

    Google Scholar 

  50. K.M. Siddiqui, K. Sahay, V. Giri, Health monitoring and fault diagnosis in induction motor-a review. Int. J.Adv.Res.Electrical Electron. Instrum.Eng. 3(1), 6549–6565 (2014)

    Google Scholar 

  51. E.T. Esfahani, S. Wang, V. Sundararajan, Multisensor wireless system for eccentricity and bearing fault detection in induction motors. IEEE/ASME Trans. Mechatron. 19(3), 818–826 (2014)

    Google Scholar 

  52. A. Kumar et al., VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing. Meas. Sci. Technol. 33, 014005 (2022)

    CAS  Google Scholar 

  53. W. Tong, Mechanical design of electric motors. (CRC Press, 2014)

    Book  Google Scholar 

  54. O. AlShorman et al., A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock. Vib. 2020, 8843759 (2020)

    Google Scholar 

  55. Z. Yang, D. Dong, H. Gao, X. Sun, Rong Fan, H. Zhu, Rotor mass eccentricity vibration compensation control in bearingless induction motor. Adv. Mech.Eng. 7(1), 168428 (2014).

    Article  Google Scholar 

  56. M. Singh, and A.G. Shaik. (2016) Bearing fault diagnosis of a three phase induction motor using stockwell transform. in India Conference (INDICON), 2016 IEEE Annual. 2016. IEEE.

  57. R. Patel, V. Giri, Condition monitoring of induction motor bearing based on bearing damage index. Arch. Electr. Eng. 66(1), 105–119 (2017)

    Google Scholar 

  58. J.J. Saucedo-Dorantes et al., Multiple-fault detection methodology based on vibration and current analysis applied to bearings in induction motors and gearboxes on the kinematic chain. Shock Vib. 2016, 1–13 (2016).

    Article  Google Scholar 

  59. M.J. Durán et al., Space-vector PWM with reduced common-mode voltage for five-phase induction motor drives. IEEE Trans. Ind. Electron. 60(10), 4159–4168 (2013)

    Google Scholar 

  60. J.B. Ali et al., Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mech. Syst. Signal Process. 56, 150–172 (2015)

    Google Scholar 

  61. I. Ishkova, and O. Vítek. (2015) Diagnosis of eccentricity and broken rotor bar related faults of induction motor by means of motor current signature analysis. in Electric Power Engineering (EPE), 2015 16th International Scientific Conference on. 2015. IEEE.

  62. M.E.K. Oumaamar et al., Static air-gap eccentricity fault diagnosis using rotor slot harmonics in line neutral voltage of three-phase squirrel cage induction motor. Mech. Syst. Signal Process. 84, 584–597 (2017)

    Google Scholar 

  63. M.N. Uddin, and M.M. Rahman. (2015) Online current and vibration signal monitoring based fault detection of bowed rotor induction motor. in Energy Conversion Congress and Exposition (ECCE), IEEE.

  64. H. Arabacı, O. Bilgin, Automatic detection and classification of rotor cage faults in squirrel cage induction motor. Neural Comput. Appl. 19(5), 713–723 (2010)

    Google Scholar 

  65. S. Altaf, M.W. Soomro, M.S. Mehmood, Fault diagnosis and detection in industrial motor network environment using knowledge-level modelling technique. Modell. Simul. Eng. 2017, 1–10 (2017).

    Article  Google Scholar 

  66. N.M. Elkasabgy, A.R. Eastham, G.E. Dawson, Detection of broken bars in the cage rotor on an induction machine. IEEE Trans. Ind. Appl. 28(1), 165–171 (1992)

    Google Scholar 

  67. A. Glowacz, Thermographic fault diagnosis of ventilation in bldc motors. Sensors. 21(21), 7245 (2021)

    Google Scholar 

  68. A. Glowacz, Ventilation diagnosis of angle grinder using thermal imaging. Sensors. 21(8), 2853 (2021)

    Google Scholar 

  69. A.H. Bonnett, Analysis of winding failures in three-phase squirrel cage induction motors. IEEE Trans.Ind.Appl. IA-14(3), 223–226 (1978)

    Google Scholar 

  70. A. Siddique, G. Yadava, B. Singh, A review of stator fault monitoring techniques of induction motors. Energy Conversion IEEE Trans. 20(1), 106–114 (2005)

    Google Scholar 

Download references


The authors acknowledge the support from Najran University Saudi Arabia and the Faculty of Electrical and Computer Engineering, Cracow University of Technology, Universiti Teknologi PETRONAS, and Sunway University Malaysia. The APC of the journal was supported by the Ministry of Science and Higher Education, Republic of Poland (grant no. E-1/2022)


The research was conducted at the Faculty of Electrical and Computer Engineering, Cracow University of Technology and Sunway University Malaysia. This research was financially supported by the Ministry of Science and Higher Education, Republic of Poland (Grant no. E-1/2022) and Sunway University REWARDING RESEARCH OUTPUT (RRO) GRTIN-RRO-03–2022.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Muhammad Aman Sheikh.

Ethics declarations

There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sheikh, M.A., Bakhsh, S.T., Irfan, M. et al. A Review to Diagnose Faults Related to Three-Phase Industrial Induction Motors. J Fail. Anal. and Preven. 22, 1546–1557 (2022).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • Induction motor
  • Squirrel cage type
  • Wound type
  • Three phases
  • Faults
  • Electrical faults
  • Mechanical faults