Development of Intelligent System for Induction Motor Fault Diagnosis in Ceiling Fan


Chaturvedi D. K.
Devendra Singh
Vikas Pratap Singh


A variety of fan faults occur in our day to day life such as electrical faults(winding faults), mechanical faults (broken rotor bars, eccentricity, bearing faults) etc. To detect the fault, many motor variables may be taken such as current, voltage, speed, sound, temperature and vibrations, so that the preventive action may be taken before the occurrence of faults in the fan. Current signature is useful for finding electrical faults such as stator faults etc. and acoustic signature is useful for finding mechanical faults such as rotor faults etc. In this paper, the on line current, voltage, rpm and temperature reading of faulty fan and healthy fan are recorded. These recorded signals are used to train a neural network so that it is able to detect the fault.


How to Cite
D. K., C., Singh, D., & Singh, V. P. (2014). Development of Intelligent System for Induction Motor Fault Diagnosis in Ceiling Fan. Power Research - A Journal of CPRI, 279–286. Retrieved from


  1. S. Edwards, A. W. Lees, and M. I. Friswell, “Fault diagnosis of rotating machinery,” The Shock and Vibration Digest, vol. 30,no. 1, pp. 4–13, 1998
  2. D. K. Chaturvedi and Himanshu Vijay, “Parameters Estimation of an Electric Fan Using ANN”, Journal of Intelligent Learning Systems and Applications, 2010, 2: 33-38.
  3. D. K. Chaturvedi and P. S. Satsangi. “Predicting the performance Characteristics of Synchronous Generators: An Alternative Approach “ J. of The Institution of Engineers (India), El 74 (1993): 109-113.
  4. L. Mann, A. Saxena, and G. M. Knapp, “Statistical-based or condition-based preventive maintenance?”Journal of Quality in Maintenance Engineering, vol. 1, no. 1, pp. 1355–2511, 1995.
  5. S. Nandi and H. A. Toliyat, “Condition monitoring and fault diagnosis of electrical machines-a review,” in Proceedings of the 34th IEEE IAS Annual Meeting on Industry Applications Conference, vol. 1, pp. 197– 204, Phoenix, Ariz, USA, October 1999.
  6. B. Marcus, “Condition based maintenance on Rail vehicle — possibilities for an innovation, Design and Product development”, Nalandalen University, Eskilsting Sweden, 2002.
  7. A. Korde, “On line condition monitoring of motors using electrical signature analysis,” Proceedings of the 4th International Conference on Engineering and Automation, Orlando, Florida, USA, July-August 2000.
  8. S. Rajakarunakaran, P. Venkumar, K. Devaraj, and K. S. P. Rao, “Artificial Neural Network Approach for Fault Detection in RotarySystem,” Appl. Soft comput., vol. 8, no. 1, pp. 740–748, 2008.
  9. H. C. Choe, Y. Wan, and A. K. Chan, “Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of cost,”CWT and DWT features, Department of Electrical Engineering, Taxes A&M University.
  10. J. Lin, M. J. Zuo, and K. R. Fyfe, “Mechanical fault detection based on the wavelet de-noising technique,” Journal of Vibration and Acoustics, vol. 126, no. 1, pp. 9–16, 2004.
  11. Chaturvedi D K, “Soft Computing Techniques and its applications in Electrical Engineering”, Studies in Computational Intelligence (SCI), Springer, 2008.
  12. H. P. Bloch and F. K. Geitner, “Machinery Failure Analysis and Troubleshooting”, chapter 5, Gulf Publishing Company, Houston, Tex, USA, 1983.