Skip to main content

Advertisement

Springer Nature Link
Log in

Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

This paper presents a fault diagnosis method for rolling bearings working in non-stationary running conditions. The proposed approach is based on an improved version of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the multivariate denoising using wavelet analysis and principal component analysis (PCA), the spectral kurtosis, and the order tracking analysis (OTA). The results show that the improved CEEMDAN has completely decomposed the raw signal into different intrinsic mode functions (IMFs) representing the natural oscillatory modes embedded into the signal. The most relevant IMF from which the defect was extracted is selected by the kurtogram plot which allows locating the optimal frequency band having the highest kurtosis value. Multivariate denoising based on wavelet analysis and PCA is used to increase the signal-to-noise ratio (SNR) of the selected IMF. The results show the great contribution of the denoising approach when comparing the selected denoised IMF with the original one. Finally, order tracking analysis is applied on the denoised IMF’s envelope to remove the effect of speed variation, and an envelope order spectrum is obtained. The proposed approach is first applied on theoretical signal simulating rolling bearing defect in variable regime including three different phases. The final order spectrum shows exactly the simulated defect order and several of its harmonics. For the experimental validation, several signals of defective rolling bearings have been measured on the Machine Fault Simulator test rig in variable regime. Despite the combined variable regime including acceleration-constant regime-deceleration, at the same time, the obtained results indicate the efficiency of the proposed method to extract the fault order with high accuracy. The maximum error between the theoretical order and the experimentally obtained one was 1.3% for outer race defect and 1% for inner race defect. Finally, the performances of the proposed method are compared to those of another diagnosis method designed for variable regime conditions. Both outer race and inner race defects are considered in acceleration regime. The results show the superiority of the proposed method to highlight the defect order with highest clarity.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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

Similar content being viewed by others

References

  1. Djebala A, Ouelaa N, Hamzaoui N (2007) Optimisation of the wavelet multiresolution analysis of shock signals: application to the signals generated by defective rolling bearings. Mech Industry 4(8):379–389

    Article  Google Scholar 

  2. Djebala A, Ouelaa N, Hamzaoui N (2008) Detection of rolling bearing defects using discrete wavelet analysis. Meccanica 43:339–348

    Article  Google Scholar 

  3. Yan R, Gao RX, Chen X (2014) Wavelets for faults diagnosis of rotary machines: a review with applications. Signal Process 96:1–15

    Article  Google Scholar 

  4. Abouelanouar B, Elamrani M, Elkihel B, Delaunois F (2018) Application of wavelet analysis and its interpretation in rotating machines monitoring and fault diagnosis. A review. Int J Eng Technol 7(4):3465–3471

    Google Scholar 

  5. Donoho DL (1995) De-noising by soft thresholding. IEEE Transact Inform Theory 41(3):613–627

    Article  MathSciNet  Google Scholar 

  6. AminGhafari M, Cheze N, Poggi JM (2006) Multivariate denoising using wavelets and principal component analysis. Comput Stat Data Anal 52(6):3061–3074

    MathSciNet  MATH  Google Scholar 

  7. Lei Y, Lin J, He Z, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35:108–126

    Article  Google Scholar 

  8. Huang NE et al (1998) The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis. Proc Roy Soc London 454A:903–995

    Article  Google Scholar 

  9. Dybala J, Zimroz R (2014) Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal. Appl Acoust 77:195–203

    Article  Google Scholar 

  10. Du Q, Yang S (2007) Application of the EMD method in the vibration analysis of ball bearings. Mech Syst Signal Process 21:2634–3644

    Article  Google Scholar 

  11. Gao Q, Duan C, Fan H, Meng Q (2008) Rotating machine fault diagnosis using empirical mode decomposition. Mech Syst Signal Process 22:1072–1081

    Article  Google Scholar 

  12. Bin GF, Gao JJ, Li XJ, Dhillon BS (2012) Early faults diagnosis of rotting machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27:696–711

    Article  Google Scholar 

  13. Pan MC, Tsao WC (2013) Using appropriate IMFs for envelope analysis in multiple fault diagnosis of ball bearings. Int J Mech Sci 69:114–124

    Article  Google Scholar 

  14. Liu X, Lin B, Luo H (2015) Bearing faults diagnostics based on hybrid LS-SVM and EMD method. Measurement 59:145–166

    Article  Google Scholar 

  15. Ahn JH, Kwak DH, Koh BH (2014) Fault detection of a roller-bearing system through the EMD of a wavelet denoised signal. Sensors 14(8):15022–15038

    Article  Google Scholar 

  16. Djebala A, Babouri MK, Ouelaa N (2015) Rolling bearing fault detection using a hybrid method based on empirical mode decomposition and optimized wavelet multi-resolution analysis. Int J Adv Manuf Technol 79(9–12):2093–2105

    Article  Google Scholar 

  17. Abdelkader R, Kaddour A, Derouiche Z (2018) Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method. Int J Adv Manuf Technol 97(5–8):3099–3117

    Article  Google Scholar 

  18. Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 1(01):1–41

    Article  Google Scholar 

  19. Wu TY, Chung YL (2009) Misalignment diagnosis of rotating machinery through vibration analysis via the hybrid EEMD and EMD approach. Smart Mater Struct 18(9):095004

    Article  Google Scholar 

  20. Guo W, Tse Peter W, Djordjevich A (2012) Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition. Measurement 45(5):1308–1322

    Article  Google Scholar 

  21. Zhang X, Zhou J (2013) Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech Syst Signal Process 41:127–140

    Article  Google Scholar 

  22. Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. Proceeding of the 36th International Conference on Acoustics, Speech and Signal Processing ICASSP 2011 (May 22-27, Prague, Czech Republic)

  23. Colominas MA, Schlotthauer G, Torres ME (2014) Improved complete ensemble EMD: a suitable tool for biomedical signal processing. J Biomed Signal Process Control 14:19–29

    Article  Google Scholar 

  24. Lei Y, Liu Z, Ouazri J, Lin J (2015) A fault diagnosis method of rolling element bearings based on CEEMDAN. Proc IMEchE C Mech Eng Sci 231(10):1804–1815

    Article  Google Scholar 

  25. Bouhalais M, Djebala A, Ouelaa N, Babouri MK (2018) CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed. Int J Adv Manuf Technol 94(5–8):2475–2489

    Article  Google Scholar 

  26. Ding F, Li X, Qu J (2017) Fault diagnosis of rolling bearing based on improved CEEMDAN and distance evaluation technique. J Vibroeng 19(1):1392–8716

    Article  Google Scholar 

  27. Jing X, Jianmin M, Zhiqiang Z, Chun C (2019) Bearing fault diagnosis based on CEEMDAN and Teager energy operator. J Phys Conf Ser 1345:032044

    Article  Google Scholar 

  28. An D, Xu B, Shao M, Li HD, Wang LY (2019) CEEMDAN-MFE method for fault extraction of rolling bearing. J Phys Conf Ser 1213:052092

    Article  Google Scholar 

  29. Capdessus C, Sekko E, and Antoni J (2014) Speed transform, a new time-varying frequency analysis technique. Advances in condition monitoring of machinery in non-stationary operations. Springer Berlin, Heidelberg 23–35

  30. Ait Sghir KA, Bolaers F, Cousinard O, Dron JP (2013) Vibratory monitoring of a spalling bearing defect in variable speed regime. Mech Industry 14(2):129–136

    Article  Google Scholar 

  31. Wu TY, Lai CH, Liu DC (2016) Defect diagnostics of roller bearing using instantaneous frequency normalization under fluctuant rotating speed. J Mech Sci Technol 30(3):1037–1048

    Article  Google Scholar 

  32. Pachaud C, Salvetat R, Fray C (1997) Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces. Mech Syst Signal Process 11(6):903–916

    Article  Google Scholar 

  33. Dron JP, Bolaers F, Rasolofondraibe L (2004) Improvement of the sensitivity of the scalar indicators (crest factor and kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings. J Sound Vib 270:61–73

    Article  Google Scholar 

  34. Antoni J (2006) The spectral kurtosis: a useful tool for characterizing non-stationary signals. Mech Syst Signal Process 20(2):282–307

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Electrical Engineering of Guelma, May 8, 1945 University of Guelma, Guelma, Algeria

    Lilia Chaabi & Ahcene Lemzadmi

  2. Laboratory of Mechanics and Structures, May 8, 1945 University of Guelma, Guelma, Algeria

    Abderrazek Djebala, Mohamed Lamine Bouhalais & Nouredine Ouelaa

Authors
  1. Lilia Chaabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahcene Lemzadmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abderrazek Djebala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Lamine Bouhalais
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nouredine Ouelaa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abderrazek Djebala.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaabi, L., Lemzadmi, A., Djebala, A. et al. Fault diagnosis of rolling bearings in non-stationary running conditions using improved CEEMDAN and multivariate denoising based on wavelet and principal component analyses. Int J Adv Manuf Technol 107, 3859–3873 (2020). https://doi.org/10.1007/s00170-020-05311-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05311-z

Keywords

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy