Reference Signal Injection in Induction Motors Drives to Electrical Failures Detection

  • Wylliam Salviano Gongora
  • Ivan Nunes da Silva
  • Alessandro GoedtelEmail author
  • Marcelo Favoretto Castoldi
  • Tiago Henrique dos Santos


This paper shows an alternative method to electrical fault diagnosis in three-phase induction motors using reference signal injection. The proposal is based on the insertion of search reference harmonic signals in the supply voltage by the frequency inverter module of the induction machine and the observation of the signals of the electric current of the motor, preprocessed through the FFT and machine signature component analysis. In order to obtain a greater precision of fault classification and state of degradation through intelligent systems, results are validated by different algorithms whose performance are compared in the following methods: k nearest neighbors, naive Bayes, support vector machine, multilayer perceptron and decision tree. The practical results for the tests with controlled simulation of the electrical faults of broken rotor bars and short circuit in stator windings show the capacity that this insertion technique has when it is used as a tool to diagnose three-phase induction motors. The five proposals of intelligent systems result in a classification with success rates of \(93\%\) to \(100\%\) in different classes, separated into types of faults and according to the degradation intensity.


Fault diagnosis Three-phase induction motors Intelligent pattern classifications Signal injection 



The authors gratefully acknowledge the contributions of CNPq (Processes 474290/2008-5, 473576/2011-2, 552269/2011-5, and 405228/2016-3), Fundação Araucária (Process 06/56093-3) and FAPESP (Process 2011/17610-0).


  1. Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., & Deliou, A. (2017). A new timefrequency method for identification and classification of ball bearing faults. Journal of Sound and Vibration, 397, 241.CrossRefGoogle Scholar
  2. Aydin, I., Karakose, M., & Akin, E. (2012). An adaptive artificial immune system for fault classification. Journal of Intelligent Manufacturing, 23(5), 1489.CrossRefGoogle Scholar
  3. Aydin, I., Karakose, M., & Akin, E. (2014). An approach for automated fault diagnosis based on a fuzzy decision tree and boundary analysis of a reconstructed phase space. ISA Transactions, 53(2), 220.CrossRefGoogle Scholar
  4. Bazan, G. H., Scalassara, P. R., Endo, W., Goedtel, A., Godoy, W. F., & Palácios, R. H. C. (2017). Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electric Power Systems Research, 143, 347.CrossRefGoogle Scholar
  5. Cusido, J., Rosero, J., Romeral, L., Ortega, J. A., & Garcia, A. (2006). New techniques for fault detection analysis by injecting additional frequency test. In Proceedings of the IEEE conference instrumentation and measurement technology (pp. 2087–2090).Google Scholar
  6. Cusido, J., Rosero, J., Romeral, L., Ortega, J. A., & Garcia, A. (2006). New fault detection techniques for induction motors. Eletrical Power Quality and Utilization, 2(1), 39.Google Scholar
  7. de Jesus Romero-Troncoso, R. (2017). Multirate signal processing to improve FFT-based analysis for detecting faults in induction motors. IEEE Transactions on Industrial Informatics, 13(3), 1291.CrossRefGoogle Scholar
  8. George, P. L., & John, H. (1995). Estimating continuous distributions in Bayesian classifiers. In Proceedings of the eleventh conference on uncertainty in artificial intelligence (pp. 338–345).Google Scholar
  9. Gritli, Y., Bellini, A., Rossi, C., Casadei, D., Filippetti, F., & Capolino, G. A. (2017). IEEE 11th international symposium on diagnostics for electrical machines, power electronics and drives (sdemped) (pp. 77–84).Google Scholar
  10. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. SIGKDD Explorations Newsletter, 11(1), 10.CrossRefGoogle Scholar
  11. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (3rd ed.). Morgan Kaufmann, p 744.Google Scholar
  12. Haykin, S. (2009). Neural networks and learning systems. Ontario: Pearson Education.Google Scholar
  13. Irfan, M., Saad, N., Ibrahim, R., & Asirvadam, V. S. (2017). Condition monitoring of induction motors via instantaneous power analysis. Journal of Intelligent Manufacturing, 28(6), 1259.CrossRefGoogle Scholar
  14. Karabadji, N. E. I., Seridi, H., Khelf, I., Azizi, N., & Boulkroune, R. (2014). Improved decision tree construction based on attribute selection and data sampling for fault diagnosis in rotating machines. Engineering Applications of Artificial Intelligence, 35, 71.CrossRefGoogle Scholar
  15. Lakehal, A., & Ramdane, A. (2017). Fault prediction of induction motor using bayesian network model. In 2017 International conference on electrical and information technologies (ICEIT) (pp. 1–5).Google Scholar
  16. Lawrynowicz, A. (2014). Pattern based feature construction in semantic data mining. International Journal on Semantic Web and Information Systems, 10, 28. Scholar
  17. Li, C., de Oliveira, J. V., Cerrada, M., Pacheco, F., Cabrera, D., Sanchez, V., et al. (2016). Observer-biased bearing condition monitoring: From fault detection to multi-fault classification. Engineering Applications of Artificial Intelligence, 50, 287.CrossRefGoogle Scholar
  18. Likitjarernkul, T., Sengchuai, K., Duangsoithong, R., Chalermyanont, K., & Prasertsit, A. (2017). Pca based feature extraction for classification of statorwinding faults in induction motors. Pertanika Journal Science and Technology, 25(S), 197.Google Scholar
  19. Liu, R., Yang, B., Zio, E., & Chen, X. (2018). Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 108, 33.CrossRefGoogle Scholar
  20. Mabrouk, A. E., & Zouzou, S. E. (2015). Diagnosis of rotor faults in three-phase induction motors under time-varying loads. In 2015 IEEE 10th international symposium on diagnostics for electrical machines, power electronics and drives (SDEMPED) (pp. 373–379).Google Scholar
  21. Martin-Diaz, I., Morinigo-Sotelo, D., Duque-Perez, O., Arredondo-Delgado, P., Camarena-Martinez, D., & Romero-Troncoso, R. (2017). Analysis of various inverters feeding induction motors with incipient rotor fault using high-resolution spectral analysis. Electric Power Systems Research, 152, 18.CrossRefGoogle Scholar
  22. Martínez-Morales, J. D., Palacios-Hernández, E. R., & Campos-Delgado, D. U. (2018). Multiple-fault diagnosis in induction motors through support vector machine classification at variable operating conditions. Electrical Engineering, 100, 1–15.CrossRefGoogle Scholar
  23. Mata-Castrejón, P. V. D., Villegas-Ortega, A., Asiaín-Olivares, T. I., & Ruiz-Vega, D. (2015). Evaluation of progressive deterioration of a squirrel-cage rotor, with a condition monitoring system that implements the sideband methodology. In 2015 IEEE international autumn meeting on power, electronics and computing (ROPEC) (pp. 1–6).Google Scholar
  24. Mustafa, M. O., Varagnolo, D., Nikolakopoulos, G., & Gustafsson, T. (2016). Detecting broken rotor bars in induction motors with model-based support vector classifiers. Control Engineering Practice, 52, 15.CrossRefGoogle Scholar
  25. Naha, A., Samanta, A. K., Routray, A., & Deb, A. K. (2016). A method for detecting half-broken rotor bar in lightly loaded induction motors using current. IEEE Transactions on Instrumentation and Measurement, 65(7), 1614.CrossRefGoogle Scholar
  26. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Francisco: Morgan Kaufmann Publishers Inc.Google Scholar
  27. Rajeswaran, N., Swarupa, M. L., Rao, T. S., & Chetaswi, K. (2018). Hybrid artificial intelligence based fault diagnosis of svpwm voltage source inverters for induction motor. Materials Today: Proceedings, 5(1, Part 1), 565.CrossRefGoogle Scholar
  28. Saddam, B., Aissa, A., Ahmed, B. S., & Abdellatif, S. (2017). Detection of rotor faults based on hilbert transform and neural network for an induction machine. In 2017 5th international conference on electrical engineering—Boumerdes (ICEE-B) (pp. 1–6).Google Scholar
  29. Singh, G., Kumar, C. A., & Naikan, V. N. A. (2015). Effectiveness of current envelope analysis to detect broken rotor bar and inter turn faults in an inverter fed induction motor drive. In 2015 international conference on power and advanced control engineering (ICPACE) (pp. 191–194).Google Scholar
  30. Steinwart, I., & Christmann, A. (2008). Support vector machines (Information Science and Statistics). New York: Springer.zbMATHGoogle Scholar
  31. Thomson, W. T., & Culbert, I. (2017). Current signature analysis for condition monitoring of cage induction motors: Industrial application and case histories (Vol. 1). Hoboken: Wiley.Google Scholar
  32. van der Broeck, H. W., Skudelny, H., & Stanke, G. V. (1988). Analysis and realization of a pulsewidth modulator based on voltage space vectors. IEEE Transactions on Industry Applications, 24(1), 142.CrossRefGoogle Scholar
  33. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.CrossRefzbMATHGoogle Scholar
  34. Yahia, K., Cardoso, A. J. M., Zouzou, S. E., & Gueddidi, S. (2012). Broken rotor bars diagnosis in an induction motor fed from a frequency converter: Experimental research. International Journal of System Assurance Engineering and Management, 3(1), 40.CrossRefGoogle Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Federal Institute of Paraná, Assis ChateaubriandAssis ChateaubriandBrazil
  2. 2.Department of Electrical Engineering, São Carlos School of EngineeringUniversity of São PauloSão CarlosBrazil
  3. 3.Federal Technological University of Paraná, Cornélio ProcópioCornélio ProcópioBrazil

Personalised recommendations