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

Ali Saghafinia; S. Hr. Kaboli

doi:doi:10.11648/j.sr.20160406.13

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

Ali Saghafinia, S. Hr. Kaboli

Published in Science Research (Volume 4, Issue 6)

Received: 24 November 2016 Accepted: 16 January 2017 Published: 7 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.

Published in	Science Research (Volume 4, Issue 6)
DOI	10.11648/j.sr.20160406.13
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), 2017. Published by Science Publishing Group

Keywords

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

References

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[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.
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[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.
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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://article.sciencepublishinggroup.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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Author Information

Ali Saghafinia

Department of Electrical Engineering, Majlesi Branch, Islamic Azad University, Majlesi, Iran
S. Hr. Kaboli

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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  -

Copy | Download