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Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm

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Abstract

This study develops an electrical detection method for the diagnosis and fault detection of induction motors. An experiment constructs two types of defect models: broken bar and dynamic eccentricity. Electrical signals acquired during the operation of a motor are transformed through a fast Fourier transform to obtain the feature frequency components for identifying the type of motor fault. Subsequently, the Clark-Concordia transform is used to compare the stator current Concordia pattern between faulty and healthy motors. Finally, a fuzzy inference system is designed for assessing the severity of motor faults. The proposed method not only can diagnose the type of motor fault, but can also assess the operational state of a motor. The method is suitable for preparing a maintenance program for induction motors and for reducing their excessive maintenance cost.

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References

  1. Lin, S.C., Chang, H.C., Kuo, C.C., Hsu, T.C., Shen, W.C.: Assessment of motor conditions using electrical and vibrational detection methods. In: International Conference on Engineering and Natural Science, pp. 41–58 (2015)

  2. Chang, H.C., Cheng, C., Lin, S.C., Li, Y.S., Kuo, C.C.: Defects identification on the stator endwinding insulation of high-voltage motor using partial discharge measurement. In: 2015 The 36th Symposium on Electrical Power Engineering (R.O.C.), pp. 1–6 (2015)

  3. Rodríguez, P.V.J., Negrea, M., Arkkio, A.: A general scheme for induction motor condition monitoring. 5th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, pp. 1–6 (2005)

  4. Nandi, S., Toliyat, H.A., Li, X.D.: Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)

    Article  Google Scholar 

  5. Concari, C., Franceschini, G., Tassoni, C.: Differential diagnosis based on multivariable monitoring to assess induction machine rotor conditions. IEEE Trans. Industr. Electron. 55(12), 4156–4166 (2008)

    Article  Google Scholar 

  6. Bellini, A., Filipprtti, F., Tassoni, C., Capolino, G.A.: Advances in diagnostic techniques for induction machines. IEEE Trans. Industr. Electron. 55(12), 4109–4126 (2008)

    Article  Google Scholar 

  7. Bruzzese, C.: Analysis and application of particular current signatures (Symptoms) for cage monitoring in nonsinusoidally fed motors with high rejection to drive load, inertia, and frequency variations. IEEE Trans. Industr. Electron. 55(12), 4137–4155 (2008)

    Article  Google Scholar 

  8. Lee, S.B., Hyun, D., Kang, T.J., Yang, C., Shin, S., Kim, H., Park, S., Kong, T.S., Kim, H.D.: Identification of false rotor fault indications produced by on-line MCSA for medium voltage induction machines. In: 2015 61st IEEE Pulp and Paper Industry Conference, pp. 1–9 (2015)

  9. Uddin, M.N., Wang, W., Huang, Z.R.: Modeling and minimization of speed ripple of a faulty induction motor with broken rotor bars. IEEE Trans. Ind. Appl. 46(6), 2243–2250 (2010)

    Article  Google Scholar 

  10. Mini, V.P., Ushakumari, S.: Incipient fault detection and diagnosis of induction motor using fuzzy logic. In: 2011 IEEE Recent Advances in Intelligent Computational Systems, pp. 675–681 (2011)

  11. Azgomi, H.F., Poshtan, J., Poshtan, M.: Experimental validation on stator fault detection via fuzzy logic. In: 2013 3rd International Conference on Electric Power and Energy Conversion Systems, pp. 1–6 (2013)

  12. Azgomi, H.F., Poshtan, J.: Induction motor stator fault detection via fuzzy logic. In: 2013 21st Iranian Conference on Electrical Engineering, pp. 1–5 (2013)

  13. Filev, D.P., Kolmanovsky, I.: Generalized markov models for real-time modeling of continuous systems. IEEE Trans. Fuzzy Syst. 22(4), 983–998 (2014)

    Article  Google Scholar 

  14. Chang, J.H., Lee, H.H.: A study on reliability based assessment algorithm for high voltage induction stator windings. In: 2012 IEEE International Power Modulator and High Voltage Conference, pp. 600–603 (2012)

  15. Amaral, T.G., Pires, V.F., Martins, J.F., Pires, A.J., Crisóstomo, M.M.: Image processing to a neuro-fuzzy classifier for detection and diagnosis of induction motor stator fault. In: 33rd Annual Conference of the IEEE Industrial Electronics Society, pp. 2408–2413 (2007)

  16. Ibrahim, F.E.A., Masoud, M.I., Mokhtari, A.E.K.: Stator inter-turn faults detection and localization using stator currents and concordia patterns-neural network applications. In: CPE ‘07 Compatibility in Power Electronics, pp. 1–7 (2007)

  17. Önel, I.Y., Benbouzid, M.E.H.: Induction motor bearing failure detection and diagnosis: park and concordia transform approaches comparative study. IEEE/ASME Trans. Mechatron. 13(2), 57–262 (2008)

    Article  Google Scholar 

  18. Das, S., Purkait, P., Chakravorti, S.: Relating stator current concordia patterns to induction motor operational abnormalities. In: TENCON 2011–2011 IEEE Region 10 Conference, pp. 933–937 (2011)

  19. Purkait, P., Das, G., Mukherjee, D., Das, S.: Influence of rotor faults on induction motor stator current Concordia patterns. In: 2012 1st International Conference on Power and Energy in NERIST, pp. 1–6 (2011)

  20. Eftekhari, M., Moallem, M., Sadri, S., Hsieh, M.F.: Online detection of induction motor’s stator winding short-circuit faults. IEEE Syst. J. 8(4), 1272–1282 (2014)

    Article  Google Scholar 

  21. Razik, H., de Rossiter Correa, M.B., Sliva, E.R.C.D.: A novel monitoring of load level and broken bar fault severity applied to squirrel-cage induction motors using a genetic algorithm. IEEE Trans. Ind. Electron. 56(11), 4615–4626 (2009)

    Article  Google Scholar 

  22. Benbouzid, M.E.H.: A review of induction motors signature analysis as a medium for faults detection. IEEE Trans. Industr. Electron. 47(5), 984–993 (2000)

    Article  Google Scholar 

  23. Zidani, F., Benbouzid, M.E.H., Diallo, D., Mohamed, S.N.S.: Induction motor stator faults diagnosis by a current concordia pattern-based fuzzy decision system. IEEE Trans. Energy Convers. 18(4), 469–475 (2003)

    Article  Google Scholar 

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Acknowledgments

The research was supported by the Ministry of Science and Technology of the Republic of China, under Grant No. MOST 103-2221-E-011-077-MY2.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Rd., Taipei, 10607, Taiwan

    Hong-Chan Chang, Shang-Chih Lin, Cheng-Chien Kuo & Cheng-Fu Hsieh

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  1. Hong-Chan Chang
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  2. Shang-Chih Lin
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  3. Cheng-Chien Kuo
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Correspondence to Hong-Chan Chang.

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Chang, HC., Lin, SC., Kuo, CC. et al. Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm. Int. J. Fuzzy Syst. 18, 732–740 (2016). https://doi.org/10.1007/s40815-016-0199-4

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  • DOI: https://doi.org/10.1007/s40815-016-0199-4

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