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Online Adaptive Continuous Wavelet Transform and Fuzzy Logic Based High Precision Fault Detection of Broken Rotor Bars for IM

Received: 24 November 2016    Accepted: 16 January 2017    Published: 07 February 2017
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Abstract

This paper presents an intelligent fault detection based on adaptive continuous wavelet transform of broken rotor bars for Induction Motor (IM). Broken rotor bars, bearing decay, eccentricity, as motor faults appears as different frequencies in the stator current signals. The stator current and speed signals at deferent operation conditions obtained from the winding function are analysed through the adaptive continuous wavelet transform (CWT) to detect the amplitudes and frequency components corresponding to different broken bar fault and load conditions. The adaptive coefficients of CWT based on the harmonics amplitude, are applied to train a fuzzy logic controller (FLC) in simulation. Then, detection of the fault condition are done based on the adaptive CWT and trained FLC in both simulation and real-time. The experimental results are confirmed the simulation results and show the effectiveness of the proposed method to detect the motor fault conditions accurately.

DOI 10.11648/j.sr.20160406.13
Published in Science Research (Volume 4, Issue 6, December 2016)
Page(s) 157-168
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Adaptive Continuous Wavelet Transform, Fault Detection, Squirrel Cage Induction Motor, Fuzzy Logic Controller, Broken Rotor Bars

References
[1] S. S. Sebtahmadi, H. Pirasteh, S. H. Aghay Kaboli, A. Radan and S. Mekhilef, "A 12-Sector Space Vector Switching Scheme for Performance Improvement of Matrix-Converter-Based DTC of IM Drive," in IEEE Transactions on Power Electronics, vol. 30, no. 7, pp. 3804-3817, July 2015.
[2] M. Riera-Guasp, M. F. Cabanas, J. A. Antonino-Daviu, M. Pineda-Sanchez, and C. Garcia, "Influence of Nonconsecutive Bar Breakages in Motor Current Signature Analysis for the Diagnosis of Rotor Faults in Induction Motors," Energy Conversion, IEEE Transactions on, vol. 25, pp. 80-89, 2010.
[3] K. S. Gaeid, H. W. Ping, M. K. Masood, and M. A. Saghafinia, "Induction motor fault tolerant control with wavelet indicator," in Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on, 2011, pp. 949-953.
[4] P. Konar and P. Chattopadhyay, "Bearing Fault Detection of Induction Motor using Wavelet and Support Vector Machines (SVM)," Applied Soft Computing, 2011.
[5] A. Saghafinia, H. W. Ping, and M. Rahman, "High performance induction motor drive using hybrid fuzzy-pi and pi controllers: A review," International Review of Electrical Engineering-Iree, vol. 5, pp. 2000-2012, 2010.
[6] S. H. Kia, H. Henao, and G. A. Capolino, "Diagnosis of broken-bar fault in induction machines using discrete wavelet transform without slip estimation," Industry Applications, IEEE Transactions on, vol. 45, pp. 1395-1404, 2009.
[7] J. Cusido, L. Romeral, J. A. Ortega, J. A. Rosero, and A. Garcia Espinosa, "Fault detection in induction machines using power spectral density in wavelet decomposition," Industrial Electronics, IEEE Transactions on, vol. 55, pp. 633-643, 2008.
[8] J. Zhang, P. Shi, and Y. Xia, "Robust adaptive sliding-mode control for fuzzy systems with mismatched uncertainties," Fuzzy Systems, IEEE Transactions on, vol. 18, pp. 700-711, 2010.
[9] A. Saghafina, H. W. Ping, M. N. Uddin, and K. S. Gaied, "Adaptive fuzzy sliding-mode control into chattering-free induction motor drive," in Industry Applications Society Annual Meeting (IAS), 2012 IEEE, 2012, pp. 1-8.
[10] A. Saghafinia, H. Ping, and M. Uddin, "Designing Self-Tuning Mechanism On Hybrid Fuzzy Controller For High Performance And Robust Induction Motor Drive," the International Journal of Advanced Technology & Engineering Research, vol. 3, 2013.
[11] A. Saghafinia and H. W. Ping, "High performance induction motor drive using fuzzy self-tuning hybrid fuzzy controller," in Power and Energy (PECon), 2010 IEEE International Conference on, 2010, pp. 468-473.
[12] A. Saghafinia, H. W. Ping, and M. N. Uddin, "Sensored field oriented control of a robust induction motor drive using a novel boundary layer fuzzy controller," Sensors, vol. 13, pp. 17025-17056, 2013.
[13] A. Saghafinia, H. W. Ping, and M. N. Uddin, "Fuzzy sliding mode control based on boundary layer theory for chattering-free and robust induction motor drive," The International Journal of Advanced Manufacturing Technology, vol. 71, pp. 57-68, 2014.
[14] A. Saghafinia, H. W. Ping, M. N. Uddin, and K. S. Gaeid, "Adaptive Fuzzy Sliding-Mode Control Into Chattering-Free IM Drive," Industry Applications, IEEE Transactions on, vol. 51, pp. 692-701, 2015.
[15] Z. H. Salih, K. S. Gaeid, and A. Saghafinia, "Sliding Mode Control of Induction Motor with Vector Control in Field Weakening," Modern Applied Science, vol. 9, p. p276, 2015.
[16] Kaboli, S. Hr A., et al. "A hybrid adaptive Neural-Fuzzy tuned PI controller based Unidirectional Boost PFC converter feeds BLDC drive." Power Electronics, Drive Systems and Technologies Conference (PEDSTC), 2013 4th. IEEE, 2013.
[17] Mansouri, Mahdi, et al. "A hybrid Neuro-Fuzzy—PI speed controller for BLDC enriched with an integral steady state error eliminator." Control System, Computing and Engineering (ICCSCE), 2012 IEEE International Conference on. IEEE, 2012.
[18] A. M. Takbash, J. Faiz, and B. M. Ebrahimi, "Losses Characterization in Voltage-Fed PWM Inverter Induction Motor Drives Under Rotor Broken Bars Fault," Magnetics, IEEE Transactions on, vol. 49, pp. 1516-1525, 2013.
[19] S. Nandi, S. Ahmed, and H. A. Toliyat, "Detection of rotor slot and other eccentricity related harmonics in a three phase induction motor with different rotor cages," Energy Conversion, IEEE Transactions on, vol. 16, pp. 253-260, 2001.
[20] W. Xu, G. Sun, G. Wen, Z. Wu, and P. K. Chu, "Equivalent Circuit Derivation and Performance Analysis of a Single-Sided Linear Induction Motor Based on the Winding Function Theory," Vehicular Technology, IEEE Transactions on, vol. 61, pp. 1515-1525, 2012.
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Author Information
  • Department of Electrical Engineering, Majlesi Branch, Islamic Azad University, Majlesi, Iran

  • UM Power Energy Dedicated Advanced Centre (UMPEDAC), Wisma R&D, University of Malaya (UM), Jalan Pantai Baharu, Kuala Lumpur, Malaysia

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  • APA Style

    Ali Saghafinia, S. Hr. Kaboli. (2017). Online Adaptive Continuous Wavelet Transform and Fuzzy Logic Based High Precision Fault Detection of Broken Rotor Bars for IM. Science Research, 4(6), 157-168. https://doi.org/10.11648/j.sr.20160406.13

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    ACS Style

    Ali Saghafinia; S. Hr. Kaboli. Online Adaptive Continuous Wavelet Transform and Fuzzy Logic Based High Precision Fault Detection of Broken Rotor Bars for IM. Sci. Res. 2017, 4(6), 157-168. doi: 10.11648/j.sr.20160406.13

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    AMA Style

    Ali Saghafinia, S. Hr. Kaboli. Online Adaptive Continuous Wavelet Transform and Fuzzy Logic Based High Precision Fault Detection of Broken Rotor Bars for IM. Sci Res. 2017;4(6):157-168. doi: 10.11648/j.sr.20160406.13

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  • @article{10.11648/j.sr.20160406.13,
      author = {Ali Saghafinia and S. Hr. Kaboli},
      title = {Online Adaptive Continuous Wavelet Transform and Fuzzy Logic Based High Precision Fault Detection of Broken Rotor Bars for IM},
      journal = {Science Research},
      volume = {4},
      number = {6},
      pages = {157-168},
      doi = {10.11648/j.sr.20160406.13},
      url = {https://doi.org/10.11648/j.sr.20160406.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sr.20160406.13},
      abstract = {This paper presents an intelligent fault detection based on adaptive continuous wavelet transform of broken rotor bars for Induction Motor (IM). Broken rotor bars, bearing decay, eccentricity, as motor faults appears as different frequencies in the stator current signals. The stator current and speed signals at deferent operation conditions obtained from the winding function are analysed through the adaptive continuous wavelet transform (CWT) to detect the amplitudes and frequency components corresponding to different broken bar fault and load conditions. The adaptive coefficients of CWT based on the harmonics amplitude, are applied to train a fuzzy logic controller (FLC) in simulation. Then, detection of the fault condition are done based on the adaptive CWT and trained FLC in both simulation and real-time. The experimental results are confirmed the simulation results and show the effectiveness of the proposed method to detect the motor fault conditions accurately.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Online Adaptive Continuous Wavelet Transform and Fuzzy Logic Based High Precision Fault Detection of Broken Rotor Bars for IM
    AU  - Ali Saghafinia
    AU  - S. Hr. Kaboli
    Y1  - 2017/02/07
    PY  - 2017
    N1  - https://doi.org/10.11648/j.sr.20160406.13
    DO  - 10.11648/j.sr.20160406.13
    T2  - Science Research
    JF  - Science Research
    JO  - Science Research
    SP  - 157
    EP  - 168
    PB  - Science Publishing Group
    SN  - 2329-0927
    UR  - https://doi.org/10.11648/j.sr.20160406.13
    AB  - This paper presents an intelligent fault detection based on adaptive continuous wavelet transform of broken rotor bars for Induction Motor (IM). Broken rotor bars, bearing decay, eccentricity, as motor faults appears as different frequencies in the stator current signals. The stator current and speed signals at deferent operation conditions obtained from the winding function are analysed through the adaptive continuous wavelet transform (CWT) to detect the amplitudes and frequency components corresponding to different broken bar fault and load conditions. The adaptive coefficients of CWT based on the harmonics amplitude, are applied to train a fuzzy logic controller (FLC) in simulation. Then, detection of the fault condition are done based on the adaptive CWT and trained FLC in both simulation and real-time. The experimental results are confirmed the simulation results and show the effectiveness of the proposed method to detect the motor fault conditions accurately.
    VL  - 4
    IS  - 6
    ER  - 

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