Skip to main content
SpringerLink
Log in

A Hierarchical Approach for the Recognition of Induction Machine Failures

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

Due to its sturdiness, low cost and ease of implementation, the induction machine is one of the most common electrical motors used in industry. However, this machine still concedes failures leading to unplanned shutdowns, sources of significant financial losses. For this reason, induction machine failure diagnosis has become an ordinary task, where some ameliorations have to be achieved in order to improve the efficiency of maintenance programs by minimizing the number and the time period of unexpected shutdowns. In this paper, we address the recognition of inter-turn short circuit (ITSC) in the stator windings and broken rotor bars (BRBs) in the three-phase squirrel cage induction machine by proposing a new approach. This approach relies on the three-phase current analysis method to extract features and a new hierarchical recognition algorithm based on an ensemble of three different classifiers. In addition, the present work studies the failures recognition of the induction machine operating with a torque and speed control. The proposed approach is a major advanced for the induction machine diagnosis research because it recognizes correctly (detects, localizes and estimates the degree of severity) ITSC and (detects and estimates) BRB faults with an accuracy of 93.36% and with a great robustness compared to a classical machine learning algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Abd-el Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332–350.

    Article  Google Scholar 

  • Ameid, T., Menacer, A., Talhaoui, H., & Harzelli, I. (2017). Broken rotor bar fault diagnosis using fast Fourier transform applied to field-oriented control induction machine: Simulation and experimental study. The International Journal of Advanced Manufacturing Technology, 92, 1–12.

    Article  Google Scholar 

  • Antonino-Daviu, J., Quijano-López, A., Climente-Alarcon, V., & Garín-Abellán, C. (2017). Reliable detection of rotor winding asymmetries in wound rotor induction motors via integral current analysis. IEEE Transactions on Industry Applications, 53(3), 2040–2048.

    Article  Google Scholar 

  • 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, 347–356.

    Article  Google Scholar 

  • Bell, R., Heising, C., O’donnell, P., Singh, C., & Wells, S. (1985a). Report of large motor reliability survey of industrial and commercial installations. II. IEEE Transactions on Industry Applications, 21(4), 865–872.

    Google Scholar 

  • Bell, R., McWilliams, D., O’donnell, P., Singh, C., & Wells, S. (1985b). Report of large motor reliability survey of industrial and commercial installations. I. IEEE Transactions on Industry Applications, 21(4), 853–864.

    Google Scholar 

  • Bessam, B., Menacer, A., Boumehraz, M., & Cherif, H. (2017). Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. International Journal of System Assurance Engineering and Management, 8(1), 478–488.

    Article  Google Scholar 

  • Da Silva, A. M., Povinelli, R. J., & Demerdash, N. A. (2008). Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Transactions on Industrial Electronics, 55(3), 1310–1318.

    Article  Google Scholar 

  • dos Santos, T., Ferreira, F. J., Pires, J. M., & Damásio, C. (2017). Stator winding short-circuit fault diagnosis in induction motors using random forest. In Proceeding of the IEEE international joint conference on electric machines and drives (pp. 1–8).

  • Eftekhari, M., Moallem, M., Sadri, S., & Hsieh, M. F. (2014). Online detection of induction motor’s stator winding short-circuit faults. IEEE Systems Journal, 8(4), 1272–1282.

    Article  Google Scholar 

  • Elbouchikhi, E., Choqueuse, V., & Benbouzid, M. (2016). Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation. ISA Transactions, 63, 413–424.

    Article  Google Scholar 

  • Glowacz, A., & Glowacz, Z. (2017). Diagnosis of the three-phase induction motor using thermal imaging. Infrared Physics & Technology, 81, 7–16.

    Article  Google Scholar 

  • Godoy, W. F., da Silva, I. N., Goedtel, A., Palácios, R. H. C., & Gongora, W. (2014). Neural approach for bearing fault classification in induction motors by using motor current and voltage. In Proceeding of the IEEE international joint conference on neural networks (pp. 2087–2092).

  • Groover, M. P. (2007). Automation, production systems, and computer-integrated manufacturing. Englewood Cliffs: Prentice Hall Press.

    Google Scholar 

  • Guedidi, S., Zouzou, S., Laala, W., Sahraoui, M., & Yahia, K. (2011). Broken bar fault diagnosis of induction motors using MCSA and neural network. In Proceeding of the IEEE international symposium on diagnostics for electric machines, power electronics & drives (pp. 632–637).

  • Haroun, S., Seghir, A. N., & Touati, S. (2017). Feature selection for enhancement of bearing fault detection and diagnosis based on self-organizing map. In M. Chadli, S. Bououden, & I. Zelinka (Eds.), Recent advances in electrical engineering and control applications (pp. 233–246). New York: Springer.

    Chapter  Google Scholar 

  • Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), 1483–1510.

    Article  Google Scholar 

  • Karmakar, S., Chattopadhyay, S., Mitra, M., & Sengupta, S. (2016). Induction motor fault diagnosis: Approach through current signature analysis. Berlin: Springer.

    Book  Google Scholar 

  • Kelleher, J. D., Mac Namee, B., & D’Arcy, A. (2015). Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Liu, Z., Cao, W., Huang, P. H., Tian, G. Y., & Kirtley, J. L. (2016). Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors. In Proceeding of the IEEE general meeting on power and energy society (pp. 1–6).

  • Lopez-Ramirez, M., Romero-Troncoso, R. J., Morinigo-Sotelo, D., Duque-Perez, O., Ledesma-Carrillo, L. M., Camarena-Martinez, D., & Garcia-Perez, A. (2016). Detection and diagnosis of lubrication and faults in bearing on induction motors through STFT. In Proceeding of the IEEE international conference on electronics, communications and computers (pp. 13–18).

  • Madescu, G., Biriescu, M., Tutelea, L. N., Mot, M., Svoboda, M., & Boldea, I. (2017). Experimental investigation of rotor currents distribution in the healthy and faulty cage of induction motors at standstill. IEEE Transactions on Industrial Electronics, 64, 5305–5313.

    Article  Google Scholar 

  • Maitre, J., Gaboury, S., Bouchard, B., & Bouzouane, A. (2015). A new computational method for stator faults recognition in induction machines based on hyper-volumes. In Proceeding of the IEEE international conference on electro information technology (pp. 216–220).

  • Malekpour, M., Phung, B., & Ambikairajah, E. (2016). Diagnosis of stator winding insulation failure in induction motors shortly after its occurrence. In Proceeding of the IEEE international conference on condition monitoring and diagnosis (pp. 485–488).

  • Merizalde, Y., Hernández-Callejo, L., & Duque-Perez, O. (2017). State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors. Energies, 10(7), 1056.

    Article  Google Scholar 

  • 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, 15–23.

    Article  Google Scholar 

  • Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors: A review. IEEE Transactions on Energy Conversion, 20(4), 719–729.

    Article  Google Scholar 

  • Ojaghi, M., Aghmasheh, R., & Sabouri, M. (2016). Model-based exact technique to identify type and degree of eccentricity faults in induction motors. IET Electric Power Applications, 10(8), 706–713.

    Article  Google Scholar 

  • Omar, T., Lahcène, N., Rachild, I., & Maurice, F. (2005). Modeling of the induction machine for the diagnosis of rotor defects. Part I. An approach of magnetically coupled multiple circuits. In Proceeding of the IEEE annual conference on industrial electronics society (p. 8).

  • Önel, I. Y., & Benbouzid, M. E. H. (2008). Induction motor bearing failure detection and diagnosis: Park and concordia transform approaches comparative study. IEEE/ASME Transactions on Mechatronics, 13(2), 257–262.

    Article  Google Scholar 

  • Palacios, R., Silva, I., Goedtel, A., Godoy, W., & Lopes, T. (2017). Diagnosis of stator faults severity in induction motors using two intelligent approaches. IEEE Transactions on Industrial Informatics, 13, 1681–1691.

    Article  Google Scholar 

  • Patel, D. C., & Chandorkar, M. C. (2014). Modeling and analysis of stator interturn fault location effects on induction machines. IEEE Transactions on Industrial Electronics, 61(9), 4552–4564.

    Article  Google Scholar 

  • Ping, Z. A., Juan, Y., & Ling, W. (2013). Fault detection of stator winding interturn short circuit in PMSM based on wavelet packet analysis. In Proceeding of the IEEE international conference on measuring technology and mechatronics automation (pp. 566–569).

  • Pires, V. F., Martins, J., Pires, A., & Rodrigues, L. (2016). Induction motor broken bar fault detection based on MCSA, MSCSA and PCA: A comparative study. In Proceeding of the IEEE international conference on compatibility, power electronics and power engineering (pp. 298–303).

  • Poyhonen, S., Negrea, M., Arkkio, A., Hyotyniemi, H., & Koivo, H. (2002). Fault diagnostics of an electrical machine with multiple support vector classifiers. In Proceeding of the IEEE international symposium on intelligent control (pp. 373–378).

  • Prudhom, A., Antonino-Daviu, J., Razik, H., & Climente-Alarcon, V. (2017). Time–frequency vibration analysis for the detection of motor damages caused by bearing currents. Mechanical Systems and Signal Processing, 84, 747–762.

    Article  Google Scholar 

  • Razavi-Far, R., Saif, M., Palade, V., & Zio, E. (2017). Adaptive incremental ensemble of extreme learning machines for fault diagnosis in induction motors. In Proceeding of the IEEE international joint conference on neural networks (pp. 1615–1622).

  • Sarkar, S., Das, S., Purkait, P., & Chakravorti, S. (2012). Application of wavelet transform to discriminate induction motor stator winding short circuit faults from incipient insulation failures. In Proceeding of the IEEE international conference on power and energy in NERIST (pp. 1–6).

  • Shi, P., Chen, Z., Vagapov, Y., & Zouaoui, Z. (2014). A new diagnosis of broken rotor bar fault extent in three phase squirrel cage induction motor. Mechanical Systems and Signal Processing, 42(1), 388–403.

    Article  Google Scholar 

  • Sobanski, P., & Orlowska-Kowalska, T. (2017). Faults diagnosis and control in a low-cost fault-tolerant induction motor drive system. Mathematics and Computers in Simulation, 131, 217–233.

    Article  MathSciNet  Google Scholar 

  • Stiller, M., Wagner, J., Thyroff, D., & Hahn, I. (2017). Comparison of different noise analysis methods for error detection on induction machines. In Proceeding of the IEEE international conference on diagnostics for electrical machines, power electronics and drives (pp. 110–116).

  • Takahashi, I., & Noguchi, T. (1986). A new quick-response and high-efficiency control strategy of an induction motor. IEEE Transactions on Industry applications, 5, 820–827.

    Article  Google Scholar 

  • Tang, H., Tan, K., & Yi, Z. (2007). Neural networks: Computational models and applications (1st Ed.). Studies in computational intelligence, Vol. 53, Artificial intelligence (incl. Robotics). Berlin, Heidelberg: Springer-Verlag.

  • Thomson, W. T., & Culbert, I. (2017). Current signature analysis for condition monitoring of cage induction motors: Industrial application and case histories. New York: Wiley.

    Google Scholar 

  • Toliyat, H. A., & Lipo, T. A. (1995). Transient analysis of cage induction machines under stator, rotor bar and end ring faults. IEEE Transactions on Energy Conversion, 10(2), 241–247.

    Article  Google Scholar 

  • Tsoumakas, G., & Katakis, I. (2006). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 64–74.

    Google Scholar 

  • Uddin, J., Kang, M., Nguyen, D. V., & Kim, J. M. (2014). Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Mathematical Problems in Engineering, 2014, 814593.

    Google Scholar 

  • Vas, P. (1998). Sensorless vector and direct torque control. Oxford: Oxford Univ. Press.

    Google Scholar 

  • Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Los Altos: Morgan Kaufmann.

    Google Scholar 

  • Yan, R., Gao, R. X., & Chen, X. (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, 1–15.

    Article  Google Scholar 

  • Yi, C., Lv, Y., Ge, M., Xiao, H., & Yu, X. (2017). Tensor singular spectrum decomposition algorithm based on permutation entropy for rolling bearing fault diagnosis. Entropy, 19(4), 139.

    Article  MathSciNet  Google Scholar 

  • Yu, Y., Zhao, Y., Wang, B., Huang, X., & Xu, D. G. (2017). Current sensor fault diagnosis and tolerant control for VSI-based induction motor drives. IEEE Transactions on Power Electronics. https://doi.org/10.1109/TPEL.2017.2713482.

Download references

Acknowledgements

We would like to thank our main financial sponsors: the Natural Sciences and Engineering Research Council of Canada, the Quebec Research Fund on Nature and Technologies, the Canadian Foundation for Innovation.

Author information

Authors and Affiliations

  1. Université du Québec à Chicoutimi, LIARA Laboratoire d’Intelligence Ambiante pour la Reconnaissance d’Activités, 555 boulevard de l’université, Chicoutimi, G7H2B1, Canada

    Julien Maitre, Abdenour Bouzouane & Sébastien Gaboury

Authors
  1. Julien Maitre
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdenour Bouzouane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sébastien Gaboury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julien Maitre.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maitre, J., Bouzouane, A. & Gaboury, S. A Hierarchical Approach for the Recognition of Induction Machine Failures. J Control Autom Electr Syst 29, 44–61 (2018). https://doi.org/10.1007/s40313-017-0353-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-017-0353-8

Keywords

Search

Navigation