SpringerLink
Log in

Frequency bearing fault detection in non-stationary state operation of induction motors using hybrid approach based on wavelet transforms and pencil matrix

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

Non-stationary fault detection under bearing fault operation of induction motor is investigated in this paper. For this aim, the vibration signal is analyzed by wavelet method and pencil matrix method. The pencil matrix (PM) or (MP) method has been combined with wavelet transform (WT), in order to reconstruct the non-stationary signal and detect the bearing fault frequency. For validation of results, an experimental setup is used for an induction motor under different load operation and with failure on its inner race. The application of the proposed technique on vibration signal under non-stationary state show that fault can be characterized by a particular signature that it is not possible with fast Fourier transform (FFT).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Srinivas RS, Tiwari R, Kannababu C (2018) Application of active magnetic bearings in flexible rotordynamic systems–a state-of-the-art review. Mech Syst Signal Process 106:537–572

    Article  Google Scholar 

  2. Moosavi SS, Djerdir A, Aït-Amirat Y, Khaburi DA (2012) Fault detection in 3-phase traction motor using artificial neural networks. In: 2012 IEEE transportation electrification conference and expo (ITEC), pp 1–6

  3. Da Y, Shi X, Krishnamurthy M (2011) Health monitoring, fault diagnosis and failure prognosis techniques for Brushless Permanent Magnet Machines. In: 2011 IEEE vehicle power and propulsion conference, pp 1–7

  4. Rosero JA, Cusido J, Garcia A, Ortega JA, Romeral L (2006) Study on the permanent magnet demagnetization fault in permanent magnet synchronous machines. In: IECON 2006–32nd annual conference on IEEE industrial electronics, pp 879–884

  5. Cipollini F, Oneto L, Coraddu A, Savio S, Anguita D (2018) Unintrusive monitoring of induction motors bearings via deep learning on stator currents. Procedia Comput Sci 144:42–51

    Article  Google Scholar 

  6. Djeddi M, Granjon P, Leprettre B (2007) Bearing fault diagnosis in induction machine based on current analysis using high-resolution technique. In: 2007 IEEE international symposium on diagnostics for electric machines power electronics and drives, pp 23–28

  7. Kanemaru M, Tsukima M, Miyauchi T, Hayashi K (2018) Bearing fault detection in induction machine based on stator current spectrum monitoring. IEEJ J Ind Appl 7(3):282–288

    Google Scholar 

  8. Schulz R, Verstockt S, Vermeiren J, Loccufier M, Stockman K, Van Hoecke S (2014) Thermal imaging for monitoring rolling element bearings. In: 12th International conference on quantitative infrared thermography (QIRT 2014). http://www.ndt.net

  9. Wang X, Mao D, Li X (2021) Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement 173:108518

    Article  Google Scholar 

  10. Zhen D, Guo J, Xu Y, Zhang H, Gu F (2019) A novel fault detection method for rolling bearings based on non-stationary vibration signature analysis. Sensors 19(18):3994

    Article  Google Scholar 

  11. Shao H, Jiang H, Zhang H, Duan W, Liang T, Wu S (2018) Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing. Mech Syst Signal Process 100:743–765

    Article  Google Scholar 

  12. Popescu TD, Aiordachioaie D (2019) Fault detection of rolling element bearings using optimal segmentation of vibrating signals. Mech Syst Signal Process 116:370–391

    Article  Google Scholar 

  13. Klausen A, Khang HV, Robbersmyr KG (2020) Multi-band identification for enhancing bearing fault detection in variable speed conditions. Mech Syst Signal Process 139:106422

    Article  Google Scholar 

  14. Ali JB, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27

    Article  Google Scholar 

  15. Verucchi CJ, Acosta GG, Benger FA (2008) A review on fault diagnosis of induction machines. Lat Am Appl Res 38(2):113–121

    Google Scholar 

  16. Blodt M, Granjon P, Raison B, Rostaing G (2008) Models for bearing damage detection in induction motors using stator current monitoring. IEEE Trans Industr Electron 55(4):1813–1822

    Article  Google Scholar 

  17. Torkaman H, Afjei SE (2010) FEM analysis of angular misalignment fault in SRM magnetostatic characteristics. Prog Electromagn Res 104:31–48

    Article  Google Scholar 

  18. Cabanas MF, González FP, Melero MG, García CHR, Orcajo GA, Rodríguez JMC, Norniella JG (2011) Insulation fault diagnosis in high voltage power transformers by means of leakage flux analysis. Prog Electromagn Res 114:211–234

    Article  Google Scholar 

  19. Thomson WT, Gilmore RJ (2003) Motor current signature analysis to detect faults in induction motor drives-fundamentals, data interpretation, and industrial case histories. In: Proceedings of the 32nd turbomachinery symposium. Texas A&M University. Turbomachinery Laboratories

  20. Riley CM, Lin BK, Habetler TG, Kliman GB (1999) Stator current harmonics and their causal vibrations: a preliminary investigation of sensorless vibration monitoring applications. IEEE Trans Ind Appl 35(1):94–99

    Article  Google Scholar 

  21. Obaid RR, Habetler TG (2003) Current-based algorithm for mechanical fault detection in induction motors with arbitrary load conditions. In: 38th IAS annual meeting on conference record of the industry applications conference IEEE, vol 2, pp 1347–1351

  22. Rezig A, Ndiye A, Djerdir A, Mekideche MR (2013) Experimental investigation of vibration monitoring technique for online detection of bearing fault in induction motors. J Electromagn Waves Appl 27(4):496–506

    Article  Google Scholar 

  23. Peeters C, Guillaume P, Helsen J (2018) Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy. Renew Energy 116:74–87

    Article  Google Scholar 

  24. Thamba NB, Himamshu HS, Nayak PK, Chiluar N (2017) Journal bearing fault detection based on daubechies wavelet. Archiv Acoust 42(3):401–414

    Article  Google Scholar 

  25. Li K, Chen P, Wang H (2012) Intelligent diagnosis method for rotating machinery using wavelet transform and ant colony optimization. IEEE Sens J 12(7):2474–2484

    Article  Google Scholar 

  26. Boudinar AH, Aimer AF, Khodja MEA, Benouzza N (2017) Induction motor’s bearing fault diagnosis using an improved short time Fourier transform. In: International conference on electrical engineering and control applications, Springer, pp 411–426

  27. Khodja MEA, Aimer AF, Boudinar AH, Benouzza N, Bendiabdellah A (2019) Bearing fault diagnosis of a PWM inverter fed-induction motor using an improved short time Fourier transform. J Electr Eng Technol 14(3):1201–1210

    Article  Google Scholar 

  28. Singru P, Krishnakumar V, Natarajan D, Raizada A (2018) Bearing failure prediction using Wigner-Ville distribution, modified Poincare map** and fast Fourier transform. J Vibroeng 20(1):127–137

    Article  Google Scholar 

  29. Ugwiri MA, Carratu M, Paciello V, Liguori C (2020) Spectral negentropy and kurtogram performance comparison for bearing fault diagnosis. In: 17th IMEKO TC 10 and EUROLAB virtual conference" global trends in testing, diagnostics and inspection for 2030". International Measurement Confederation (IMEKO), pp 105–110

  30. **e P, Yang YX, Jiang GQ, Du YH, Li XL (2012) A newfault detection and diagnosis method based on Wigner-Ville spectrum entropy for the rolling bearing. In: Applied mechanics and materials, vol 197. Trans Tech Publications Ltd, pp 346–350

  31. Djaballah S, Meftah K, Khelil K, Ted**i M, Sedira L (2019) Detection and diagnosis of fault bearing using wavelet packet transform and neural network. Frattura ed Integrità Strutturale 13(49):291–301

    Article  Google Scholar 

  32. Zhang X, Zhu J, Wu Y, Zhen D, Zhang M (2020) Feature extraction for bearing fault detection using wavelet packet energy and fast Kurtogram analysis. Appl Sci 10(21):7715

    Article  Google Scholar 

  33. Bessam B, Menacer A, Boumehraz M, Cherif H (2017) Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. Int J Syst Assur Eng Manag 8(1):478–488

    Article  Google Scholar 

  34. Terriche Y et al (2020) A resolution-enhanced sliding matrix pencil method for evaluation of harmonics distortion in shipboard microgrids. IEEE Trans Transp Electrif 6(3):1290–1300

    Article  Google Scholar 

  35. Terriche Y, Laib A, Lashab A, Su C-L, Guerrero JM, Vasquez JC (2022) A frequency independent technique to estimate harmonics and interharmonics in shipboard microgrids. IEEE Trans Smart Grid 13(2):888–899. https://doi.org/10.1109/TSG.2021.3128554

    Article  Google Scholar 

  36. Sheshyekani K, Karami HR, Dehkhoda P, Paolone M, Rachidi F (2012) Application of the matrix pencil method to rational fitting of frequency-domain responses. IEEE Trans Power Delivery 27(4):2399–2408

    Article  Google Scholar 

  37. Guglielmi N, Lubich C, Mehrmann V (2017) On the nearest singular matrix pencil. SIAM J Matrix Anal Appl 38(3):776–806

    Article  MathSciNet  Google Scholar 

  38. Chahine K (2018) Rotor fault diagnosis in induction motors by the matrix pencil method and support vector machine. Int Trans Electr Energy Syst 28(10):e2612

    Article  Google Scholar 

  39. Patel RK, Agrawal S, Joshi NC (2012) Induction motor bearing fault identification using vibration measurement. In: 2012 Students conference on engineering and systems. IEEE, pp 1–5

  40. Wang H, Yu Z, Guo L (2020) Real-time online fault diagnosis of rolling bearings based on KNN algorithm. J Phys Conf Ser 1486(3):032019

    Article  Google Scholar 

  41. Lin HC, Ye YC (2019) Reviews of bearing vibration measurement using fast Fourier transform and enhanced fast Fourier transform algorithms. Adv Mech Eng 11(1):1687814018816751

    Article  Google Scholar 

  42. Deng W, Yao R, Zhao H, Yang X, Li G (2019) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput 23(7):2445–2462

    Article  Google Scholar 

  43. Zhang G, Xu H, Zhang T (2021) Method of rolling bearing fault detection based on two-dimensional tri-stable stochastic resonance system. J Vib Eng Technol 9(1):61–72

    Article  Google Scholar 

  44. Liu F, Gao J, Liu H (2020) The feature extraction and diagnosis of rolling bearing based on CEEMD and LDWPSO-PNN. IEEE Access 8:19810–19819

    Article  Google Scholar 

  45. Zeng S, Lu G, Yan P (2019) Vibration feature extraction using local temporal self-similarity for rolling bearing fault diagnosis. In: 2019 IEEE international conference on prognostics and health management (ICPHM), pp 1–5

  46. Li W, Cao Y, Li L, Hou S (2022) An orthogonal wavelet transform-based K-nearest neighbor algorithm to detect faults in bearings. Shock Vib

Download references

Author information

Authors and Affiliations

  1. L2EI Laboratory, Jijel University, Jijel, Algeria

    I. Bouaissi, A. Rezig & M. Mellit

  2. Department of Electrical Engineering Faculty of Technology, University of M’sila, Ichebilia, PO Box 166, 2800, M’sila, Algeria

    A. Laib & A. N’diaye

  3. Nuclear Research Center, Birine, Algeria

    S. Touati

  4. FEMTO-ST Laboratory, UTBM, Belfort, France

    A. Djerdir

Authors
  1. I. Bouaissi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Laib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Rezig
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Mellit
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Touati
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. A. Djerdir
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. A. N’diaye
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Bouaissi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bouaissi, I., Laib, A., Rezig, A. et al. Frequency bearing fault detection in non-stationary state operation of induction motors using hybrid approach based on wavelet transforms and pencil matrix. Electr Eng (2024). https://doi.org/10.1007/s00202-023-02235-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-023-02235-1

Keywords

Search

Navigation