Advertisement

Neural Computing and Applications

, Volume 31, Supplement 1, pp 683–691 | Cite as

Analysis of distributed faults in inner and outer race of bearing via Park vector analysis method

  • Muhammad IrfanEmail author
  • Nordin Saad
  • Rosdiazli Ibrahim
  • Vijanth S. Asirvadam
  • A. Alwadie
Original Article
  • 119 Downloads

Abstract

The Park’s transformation technique for diagnosing and statistically analyzing a variety of bearing faults is introduced in this paper. The currently used stator current analysis and instantaneous power analysis methods are not capable of diagnosing bearing distributed faults, because the defect frequency model is not available for this kind of faults. Notably, this paper has been aimed at developing a system for the non-invasive condition monitoring of bearing distributed defects on the basis of the Park vector analysis. It is also aimed at statistically evaluating the ability of this developed system to not only analyze but also segregate the localized and distributed faults in the bearings. The theoretical as well as experimental work that has been carried out demonstrates that the proposed technique can not only diagnose both types of bearing faults, but also classify them. The effectiveness of the proposed technique has been confirmed by the results obtained from real hardware implementation.

Keywords

Bearing Localized faults Distributed faults Condition monitoring Intelligent diagnostics 

Notes

Acknowledgements

The authors acknowledge the support from Universiti Teknologi PETRONAS for the award of Universiti Research Innovation Fund (URIF-0153-B87) and Ministry of Higher Education (MOHE) Malaysia for the award of the Prototype Research Grant Scheme (PRGS).

Compliance with ethical standards

Conflict of interest

The authors have no conflict of interest.

References

  1. 1.
    Ojaghi M, Sabouri M, Faiz J (2014) Diagnosis methods for stator winding faults in three-phase squirrel-cage induction motors. Int Trans Electr Energy Syst 24(6):891–912CrossRefGoogle Scholar
  2. 2.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2013) An intelligent diagnostic condition monitoring system for ac motors via instantaneous power analysis. Int Rev Electr Eng 8(2):664–672Google Scholar
  3. 3.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2013) An intelligent diagnostic system for condition monitoring of ac motors. In: The 8th IEEE conference on industrial electronics and applications, Melbourne, AustraliaGoogle Scholar
  4. 4.
    Seera M, Lim CP, Ishak D, Singh H (2013) Application of the fuzzy min–max neural network to fault detection and diagnosis of induction motors. Neural Comput Appl 23(1):191–200CrossRefGoogle Scholar
  5. 5.
    Su H, Chong K, Ravi Kumar R (2011) Vibration signal analysis for electrical fault detection of induction machine using neural networks. Neural Comput Appl 20(2):183–194CrossRefGoogle Scholar
  6. 6.
    Cusido J, Romeral L, Garcia-Espinosa A, Ortega JA, Riba-Ruiz J-R (2011) On-line fault detection method for induction machines based on signal convolution. Eur Trans Electr Power 21:475–488. doi: 10.1002/etep.455 CrossRefGoogle Scholar
  7. 7.
    Xie Y, Gu C, Cao W (2013) Study of broken bars in three-phase squirrel-cage induction motors at standstill. Int Trans Electr Energy Syst 23(7):1124–1138CrossRefGoogle Scholar
  8. 8.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS, Hung NT (2014) Analysis of bearing outer race defects in induction motor. In: The 5th IEEE international conference on intelligent and systems (ICIAS), Kuala Lumpur, MalaysiaGoogle Scholar
  9. 9.
    Tavner PJ, Ran L, Pennman J, Sedding H (2008) Condition monitoring of rotating electrical machines. Research Studies Press Ltd., LetchworthCrossRefGoogle Scholar
  10. 10.
    Eren L, Devaney MJ (2004) Bearing damage detection via wavelet packet decomposition of the stator current. IEEE Trans Instrum Meas 53(2):431–436CrossRefGoogle Scholar
  11. 11.
    Kia SH, Henao H, Capolino G (2007) A high-resolution frequency estimation method for three-phase induction machine fault detection. IEEE Trans Ind Electron 54(4):2305–2314CrossRefGoogle Scholar
  12. 12.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS, Hung NT (2014) A non-invasive fault diagnosis system for induction motors in noisy environment. In: IEEE international conference on power and energy (PECon), Kuching, Malaysia, pp 271–276Google Scholar
  13. 13.
    Zhou W, Habetler TG, Harley RG (2008) Bearing fault detection via stator current noise cancellation and statistical control. IEEE Trans Ind Electron 55(12):4260–4469CrossRefGoogle Scholar
  14. 14.
    Singh S, Köpke UG, Howard CQ, Petersen D (2014) Analyses of contact forces and vibration response for a defective rolling element bearing using an explicit dynamics finite element model. J Sound Vib 333:5356–5377CrossRefGoogle Scholar
  15. 15.
    Singh S, Howard CQ, Hansen CH (2015) An extensive review of vibration modelling of rolling element bearings with localised and extended defects. J Sound Vib 357:300–330CrossRefGoogle Scholar
  16. 16.
    Zhang P, Du Y, Habetler TG, Lu B (2011) A survey of condition monitoring and protection methods for medium-voltage induction motors. IEEE Trans Ind Appl 47(1):34–46CrossRefGoogle Scholar
  17. 17.
    Gandhi A, Corrigan T, Parsa L (2011) Recent advances in modeling and online detection of stator interturn faults in electrical motors. IEEE Trans Industr Electron 58(5):1564–1575CrossRefGoogle Scholar
  18. 18.
    Heng A, Zhang S, Tan ACC, Mathew J (2009) Rotating machinery prognostics: state of the art, challenges and opportunities. Mech Syst Signal Process 23:724–739CrossRefGoogle Scholar
  19. 19.
    Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques part I: fault diagnosis with model based and signal-based approaches. IEEE Trans Industr Electron 62:3757–3767CrossRefGoogle Scholar
  20. 20.
    Hurtado ZYM, Tello CP, Sarduy JG (2014) A review on detection and fault diagnosis in induction machines. Publicaciones en Ciencias y Tecnologa 8(01)Google Scholar
  21. 21.
    Mehala N (2010) Condition monitoring and fault diagnosis of induction motor using motor current signature analysis PhD Thesis, National Institute of Technology Kurukshetra, IndiaGoogle Scholar
  22. 22.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2015) An online condition monitoring system for induction motors via instantaneous power analysis. J Mech Sci Technol 29(4):1483–1492CrossRefGoogle Scholar
  23. 23.
    Shah DS, Patel VN (2014) A review of dynamic modeling and fault identifications methods for rolling element bearing. In: 2nd International conference on innovations in automation and mechatronics engineering, ICIAMEGoogle Scholar
  24. 24.
    Dolenc B, Boškoski P, Juričić Đ (2015) Distributed bearing fault diagnosis based on vibration analysis. Mech Syst Signal Process 66:521–532Google Scholar
  25. 25.
    Dalvand F, Keshavarzi M, Kalantar A, Cheraghdar A (2015) Detection of generalized-roughness bearing fault using statistical-time indices of instantaneous frequency of motor voltage space vector. In: 23rd Iranian conference on electrical engineering (ICEE)Google Scholar
  26. 26.
    Salem SB, Touti W, Bacha K, Chaari A (2013) Induction motor mechanical fault identification using park vector approach. In: International conference on electrical engineering and software applications (ICEESA)Google Scholar
  27. 27.
    Zarei J, Poshtan J (2009) An advanced Park’s vectors approach for bearing fault detection. Tribol Int 19:213–219CrossRefGoogle Scholar
  28. 28.
    Onel IY, Benbouzid MEH (2008) Induction motor bearing failure detection and diagnosis: park and concordia transform approaches comparative study. IEEE/ASME Trans Mechatron 13(2):257–262CrossRefGoogle Scholar
  29. 29.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2015) An approach to diagnose inner race surface roughness faults in bearings of induction motors. In: IEEE International Conference on Signal and Image Analysis (ICSIPA), Kuala Lumpur, MalaysiaGoogle Scholar
  30. 30.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2016) An online fault diagnosis system for induction motors via instantaneous power analysis. https://scholar.google.com/citations?view_op=view_citation&hl=en&user=3Ovr9eQAAAAJ&citation_for_view=3Ovr9eQAAAAJ:Tyk-4Ss8FVUC. Tribol Trans. doi: 10.1080/10402004.2016.1190043
  31. 31.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2015) Condition monitoring of induction motors via instantaneous power analysis. J Intell Manuf. doi: 10.1007/s10845-015-1048-2 Google Scholar
  32. 32.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2016) A non invasive method for condition monitoring of induction motors operating under arbitrary loading conditions. Arab J Sci Eng 41(9):3463–3471CrossRefGoogle Scholar
  33. 33.
    Irfan M, Saad N, Ibrahim R, Asirvadam VS (2015) Analysis of bearing surface roughness defects in induction motors. J Fail Anal Prev 15(5):730–736CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Muhammad Irfan
    • 1
    Email author
  • Nordin Saad
    • 2
  • Rosdiazli Ibrahim
    • 2
  • Vijanth S. Asirvadam
    • 2
  • A. Alwadie
    • 1
  1. 1.Electrical Engineering Department, Faculty of EngineeringNajran UniversityNajranKingdom of Saudi Arabia
  2. 2.Department of Electrical and Electronics EngineeringUniversiti Teknologi PetronasBandar Seri IskandarMalaysia

Personalised recommendations