Journal of Failure Analysis and Prevention

, Volume 15, Issue 5, pp 730–736 | Cite as

Analysis of Bearing Surface Roughness Defects in Induction Motors

  • Muhammad Irfan
  • Nordin Saad
  • Rosdiazli Ibrahim
  • Vijanth S. Asirvadam
  • N. T. Hung
  • Muawia A. Magzoub
Technical Article---Peer-Reviewed

Abstract

In this paper, a Park’s transformation method for the analysis of various bearing surface roughness defects is presented. The existing instantaneous power analysis and stator current analysis techniques are unable to diagnose bearing surface roughness defects, due to the fact that characteristics defect frequency model is not available for these types of defects. Thus, this paper proposes a Park’s transformation method which can detect surface roughness defects without requiring information of the characteristic defect frequencies. The theoretical and experimental work conducted shows that the proposed method can detect bearing outer and inner race surface roughness faults without use of any extra hardware. The results on the real hardware implementation confirm the effectiveness of the proposed approach.

Keywords

Bearing surface roughness faults Condition monitoring Intelligent diagnostics Machine vibration 

Notes

Acknowledgment

The authors acknowledge the support from Universiti Teknologi PETRONAS and Ministry of Higher Education (MOHE) Malaysia for the award of the Exploratory Research Grant Scheme (ERGS /1/2012/TK02/UTP/02/09).

References

  1. 1.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, An online condition monitoring system for induction motors via instantaneous power analysis. J. Mech. Sci. Technol. 29(4), 1483–1492 (2015)CrossRefGoogle Scholar
  2. 2.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, An intelligent diagnostic condition monitoring system for AC motors via instantaneous power analysis. Int. Rev. Electr. Eng. 8(2), 664–672 (2013)Google Scholar
  3. 3.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, An intelligent diagnostic system for condition monitoring of AC motors, in The 8th IEEE Conference on Industrial Electronics and Applications, Melbourne, Australia, June 2013.Google Scholar
  4. 4.
    S.D. Choi, B. Akin, M. Rahimian, H.A. Toliyat, Implementation of fault diagnosis algorithm for induction machines based on advanced digital signal processing techniques. IEEE Trans. Ind. Electron. 58(3), 937–948 (2011)CrossRefGoogle Scholar
  5. 5.
    L.M.C. Medina, R. de Jesus Romero-Troncoso, E. Cabal-Yepez, J. de Jesus Rangel-Magdaleno, J.R. Millan-Almaraz, FPGA based multiple-channel vibration analyzer for industrial applications in induction motor failure detection. IEEE Trans. Instrum. Meas. 59(1), 63–72 (2010)CrossRefGoogle Scholar
  6. 6.
    J. Cusido, L. Romeral, A. Garcia-Espinosa, J.A. Ortega, J.-R. Riba-Ruiz, On-line fault detection method for induction machines based on signal convolution. Eur. Trans. Electr. Power 21, 475–488 (2011). doi: 10.1002/etep.455 CrossRefGoogle Scholar
  7. 7.
    Ying Xie, Gu Chenglin, Wenping Cao, Study of broken bars in three-phase squirrel-cage induction motors at standstill. Int. Trans. Electr. Energy Syst. 23(7), 1124–1138 (2013)CrossRefGoogle Scholar
  8. 8.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.T. Hung, Analysis of bearing outer race defects in induction motor, in The 5th IEEE International Conference on Intelligent and Systems (ICIAS), Kualalumpur, Malaysia, June 2014Google Scholar
  9. 9.
    P.J. Tavner, L. Ran, J. Pennman, H. Sedding, Condition Monitoring of Rotating Electrical Machines (Research Studies Press Ltd., Letchworth, 2008)CrossRefGoogle Scholar
  10. 10.
    L. Eren, M.J. Devaney, Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans. Instrum. Meas. 53(2), 431–436 (2004)CrossRefGoogle Scholar
  11. 11.
    S.H. Kia, H. Henao, G. Capolino, A high-resolution frequency estimation method for three-phase induction machine fault detection. IEEE Trans. Ind. Electron. 54(4), 2305–2314 (2007)CrossRefGoogle Scholar
  12. 12.
    M. Irfan, N. Saad, R. Ibrahim, V.S. Asirvadam, N.T. Hung, A non-invasive fault diagnosis system for induction motors in noisy environment, in IEEE International Conference on Power and Energy (PECon), Kuching, Malaysia, December 2014, pp. 271–276Google Scholar
  13. 13.
    W. Zhou, T.G. Habetler, R.G. Harley, Bearing fault detection via stator current noise cancellation and statistical control. IEEE Trans. Ind. Electron. 55(12), 4260–4469 (2008)CrossRefGoogle Scholar
  14. 14.
    S. Nandi, H.A. Toliyat, X. Li, Condition monitoring and fault diagnosis of electrical machines—a review. IEEE Trans. Energy Convers. 20(04), 719–729 (2005)CrossRefGoogle Scholar
  15. 15.
    M. Ojaghi, M. Sabouri, J. Faiz, Diagnosis methods for stator winding faults in three-phase squirrel-cage induction motors. Int. Trans. Electr. Energy Syst. 24(6), 891–912 (2014)CrossRefGoogle Scholar
  16. 16.
    L. Hou, N.W. Bergmann, Novel industrial wireless sensor networks for machine condition monitoring and fault diagnosis. IEEE Trans. Instrum. Meas. 61(10), 2787–2798 (2012)CrossRefGoogle Scholar
  17. 17.
    M. Blodt, P. Granjon, B. Raison, G. Rostaing, Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans. Ind. Electron. 55(4), 1813–1822 (2008)CrossRefGoogle Scholar
  18. 18.
    E.C. Yepez, A.A.F. Jaramillo, J.M.L. Garcia, Real-time condition monitoring on VSD-fed induction motors through statistical analysis and synchronous speed observation. Int. Trans. Electr. Energy Syst. (2014). doi: 10.1002/etep.1938 Google Scholar
  19. 19.
    W. Zhou, T.G. Habetler, R.G. Harley, Bearing condition monitoring methods for electric machines: a general review, in IEEE International System Diagnostics Electric Machines & Power Electronics Drives, 2007Google Scholar
  20. 20.
    S.B. Salem, W. Touti, K. Bacha, A. Chaari, Induction motor mechanical fault identification using park vector approach, in International Conference on Electrical Engineering and Software Applications (ICEESA), March 2013Google Scholar
  21. 21.
    N. Mehala, Condition monitoring and fault diagnosis of induction motor using motor current signature analysis, PhD Thesis, National Institute of Technology Kurukshetra, India, 2010Google Scholar
  22. 22.
    J. Zarei, J. Poshtan, An advanced Park’s vectors approach for bearing fault detection, in IEEE International Conference on Industrial Technology, December 15–17, 2006, pp. 1472–1479Google Scholar
  23. 23.
    I.Y. Onel, M.E.H. Benbouzid, Induction motor bearing failure detection and diagnosis: park and concordia transform approaches comparative study. IEEE/ASME Trans. Mechatron. 13(2), 257–262 (2008)CrossRefGoogle Scholar

Copyright information

© ASM International 2015

Authors and Affiliations

  • Muhammad Irfan
    • 1
  • Nordin Saad
    • 1
  • Rosdiazli Ibrahim
    • 1
  • Vijanth S. Asirvadam
    • 1
  • N. T. Hung
    • 1
  • Muawia A. Magzoub
    • 1
  1. 1.Department of Electrical and Electronics EngineeringUniversiti Teknologi PETRONASBandar Seri IskandarMalaysia

Personalised recommendations