International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Recent trends on IOT based condition monitoring of AC motors: a review

Author Affiliations

  • 1Department of Electrical and Electronics Engineering, Bhilai Institute of Technology Raipur, Raipur, 493661, Chhattisgarh, India
  • 2Department of Electrical Engineering, Bhilai Institute of Technology Durg-491001, Chhattisgarh, India

Res. J. Engineering Sci., Volume 10, Issue (2), Pages 20-23, May,26 (2021)


Rotating electrical machines are widely used in every manufacturing industry. Maintenance schedule and repair of AC motors are of utmost importance for industrial sectors. There has been considerable growth in methods of condition monitoring for motors and its predictive maintenance. In this paper recent technologies will be discussed where all the parameters like temperature, current, vibration & others are monitored wirelessly with the help of internet connectivity. This paper presents the review of various IOT based system used for data acquisition from sensors and its storage in cloud. The real time monitoring of motors is also done with graphical interface available in web server and APIS. The data stored in cloud as history can be used for making mathematical models which can predict the future faults in motors and in conjunction to that maintenance schedule can be generated. The review of various methods will help researchers in analyzing available IOT & wireless based system in condition monitoring and failure prediction of AC rotating electrical machines.


  1. Narwade, S., Kulkarni, P., & Patil, C.Y. (2014)., Fault Detection of Induction Motor Using Current and Vibration Monitoring.,
  2. Shyamala D., Swathi D., Prasanna J. L. and Ajitha A. (2017)., IoT platform for condition monitoring of industrial motors., 2nd International Conference on Communication and Electronics Systems (ICCES). pp 260-265.
  3. Medina-Garcia, J., Sanchez-Rodriguez, T., Galan, J. A. G., Delgado, A., Gomez-Bravo, F. and Jimenez, R. (2017)., A wireless sensor system for real-time monitoring and fault detection of motor arrays., Sensors, 17(3), 469.
  4. Şen M. & Kul B. (2017)., IoT-based wireless induction motor monitoring., XXVI International Scientific Conference Electronics. Bulgaria, 13th-15th Sep. pp 1-5.
  5. Kunthong J., Sapaklom T., Konghirun M., Prapanavarat C., Ayudhya P.N., Mujjalinvimut E. and Boonjeed S. (2017)., IoT-Based Traction Motor Drive Condition Monitoring in Electric Vehicles: Part 1., IEEE PEDS.
  6. Kumar, P., Winston P., Yuvrani, B., Sugandha, R., Sheelarajasri, Manikkaathiga, S. (2018)., Identification of a Fault and Real Time Monitoring System for AN AC Motor Using IOT., International Journal of Advance Research in Engineering Science & Technology, 5(4), pp 553-558.
  7. Magadan L., Suarez F. J., Granda, J. C. and Garcia D. F. (2020)., Low-cost real-time monitoring of electric motors for the Industry 4.0., International conference on Industry 4.0 and smart Manufacturing, 42, pp 393-398.
  8. Chauhan A., Gangsar P., Porwal R. and Mechefske C. (2020)., Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions., Vibro engineering Procedia. 30
  9. Venugopal K., Madhusudan P. and Amrutha A. (2017)., Artificial Neural Network based Fault Prediction Framework for Transformers in Power Systems., Proceedings of the IEEE 2017 International Conference on Computing Methodologies and Communication. pp 520-523.
  10. Ballal M.S., Khan Z.J., Mishra. M.K. and Sonolikar. R.L. (2004)., Artificial Neural Network approach for the incipient faults detection in single phase induction motors., National Power Systems Conference. 27th-30th Dec. pp 27-30
  11. Firmansah A., Aripribarta, Mufti N., Affandi A.N. and Zaini I. (2018)., Self-powered IoT Based Vibration Monitoring of Induction Motor for Diagnostic and Prediction Failure., IOP Conf. Series: Materials Science and Engineering 588
  12. Tian Y., Guo D., Zhang K., Jia L., Qiao H. and Tang H. (2018)., A Review of Fault Diagnosis for Traction Induction Motor., Proceedings of the 37th Chinese Control Conference. Wuhan, China, 25-27 July, pp 5763-5768.
  13. Kolhe, M. S., & Tapre, M. P. C. (2019)., Condition Monitoring & Control of Induction Motors by using IoT Platform for Agriculture System., IJERT, 8(07), 1043-1045.
  14. Dash R.N., Sahu, S., Panigrahi C.K. and Subudhi B. (2016)., Condition Monitoring of Induction Motors: - A Review., International conference on Signal Processing, Communication, Power and Embedded System (SCOPES). Pp 2006-2011.
  15. Jose, G., & Jose, V. (2013)., Induction motor fault diagnosis methods: A comparative study., In International conference on electrical engineering (ICEE-2013), pp. 863-866.
  16. Ghate, V. N., & Dudul, S. V. (2009)., Fault diagnosis of three phase induction motor using neural network techniques., In 2009 Second International Conference on Emerging Trends in Engineering & Technology (pp. 922-928). IEEE.
  17. Muvvala, K., Nair A., Mangrulkar, A. and Mistry, H. (2018)., Condition Based monitoring system using IoT., International Journal of Applied Engineering Research, 13(12), 10186-10190.
  18. Gajbiye, A., Zodpe, P., Abbas Z. and Patanwala, H. (2019)., IoT Based Condition Monitoring of An Induction Motor., IOSRJEN, 33-40.