Skip to main content
SpringerLink
Log in

Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method

  • Original Paper
  • Published:
Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

The vibration signals captured for rolling bearing are generally polluted by excessive noise and can lose the fault information at the early development phase. Therefore, denoising is required to enhance the signal quality by improving kurtosis parameter sensitivity and envelope spectrum performance for early fault detection.

Methods

In this paper, an improved denoising technique is proposed for early faults diagnosis of rolling bearing based on the complete ensemble empirical mode decomposition (CEEMD) and adaptive thresholding (ATD) method. Firstly, the bearing vibration signals are decomposed into a set of various intrinsic mode functions (IMFs) using the CEEMD algorithm. The IMFs grouping and selection are formed based upon the approximate entropy and correlation coefficient value. After IMFs selection, the noise-predominant IMFs are subjected to adaptive thresholding for denoising and then added to the low-frequency IMFs for signal reconstruction.

Results

The effectiveness of the proposed method denoised signals are tested on two experimental datasets based on kurtosis value and the envelope spectrum analysis. The results on the first dataset shows significant improvement in the kurtosis parameter values such as 92.99, 90.92, 97.39, and 78.35 for inner race fault in the bearing and 113.1, 170, 195.1, and 197.4 for outer race fault in the bearing rotating at four different speeds of 1797, 1772, 1750, and 1730 rpm, respectively, and enhances the amplitude of envelope spectrum to easily detect the bearing fault characteristics frequencies. The developed methods are further tested on the second dataset yielding similar improvement in kurtosis value and envelope spectrum analysis.

Conclusion

The presented method results on experimental datasets illustrate that the proposed approach is an effective denoising technique for early fault detection in the rolling bearing compared to the original and other two conventional approaches.

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

Access this article

Log in via an institution

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
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33

Similar content being viewed by others

Availability of Data and Material

The data and material that support the findings of this study are openly available in the data repository of Case Western Reserve University and the Society for Machinery Failure Prevention Technology.

Code Availability

Not applicable.

References

  1. Wei Y, Li Y, Xu M, Huang W (2019) A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy 21(4):1–26. https://doi.org/10.3390/e21040409

    Article  Google Scholar 

  2. Gundewar SK, Kane PV (2021) Condition monitoring and fault diagnosis of induction motor. J Vib Eng Technol 9(4):643–674. https://doi.org/10.1007/s42417-020-00253-y

    Article  Google Scholar 

  3. Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012

    Article  Google Scholar 

  4. Heng RBW, Nor MJM (1998) Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Appl Acoust 53(1–3):211–226. https://doi.org/10.1016/s0003-682x(97)00018-2

    Article  Google Scholar 

  5. Aasi A, Tabatabaei R, Aasi E, Jafari SM (2021) Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission. JVC J Vib Control. https://doi.org/10.1177/10775463211016130

    Article  Google Scholar 

  6. AlShorman O et al (2021) Sounds and acoustic emission-based early fault diagnosis of induction motor: a review study. Adv Mech Eng 13(2):1–19. https://doi.org/10.1177/1687814021996915

    Article  Google Scholar 

  7. Martin HR, Honarvar F (1995) Application of statistical moments to bearing failure detection. Appl Acoust 44(1):67–77. https://doi.org/10.1016/0003-682X(94)P4420-B

    Article  Google Scholar 

  8. Antoni J, Randall RB (2006) The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech Syst Signal Process 20(2):308–331. https://doi.org/10.1016/j.ymssp.2004.09.002

    Article  Google Scholar 

  9. McFadden PD, Smith JD (1984) Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol Int 17(1):3–10. https://doi.org/10.1016/0301-679X(84)90076-8

    Article  Google Scholar 

  10. Huang NE et al (1998) The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193

    Article  MathSciNet  MATH  Google Scholar 

  11. Rai VK, Mohanty AR (2007) Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mech Syst Signal Process 21(6):2607–2615. https://doi.org/10.1016/j.ymssp.2006.12.004

    Article  Google Scholar 

  12. Zhao D, Li J, Cheng W, Wang T, Wen W (2016) Rolling element bearing instantaneous rotational frequency estimation based on EMD soft-thresholding denoising and instantaneous fault characteristic frequency. J Cent South Univ 23(7):1682–1689. https://doi.org/10.1007/s11771-016-3222-x

    Article  Google Scholar 

  13. Yang D, Sun Y, Wu K (2020) Research on CEEMD-AGA denoising method and its application in feed mixer. Math Probl Eng 2020:1–9

    Google Scholar 

  14. Hu YF, Li Q (2021) An adjustable envelope based EMD method for rolling bearing fault diagnosis. IOP Conf Ser Mater Sci Eng 1043(3):2021. https://doi.org/10.1088/1757-899X/1043/3/032017

    Article  Google Scholar 

  15. Zhu K, Song X, Xue D (2013) Incipient fault diagnosis of roller bearings using empirical mode decomposition and correlation coefficient. J Vibroeng 15(2):597–603

    Google Scholar 

  16. Tabatabaei R, Aasi A, Jafari SM (2020) Experimental investigation of the diagnosis of angular contact ball bearings using acoustic emission method and empirical mode decomposition. Adv Tribol. https://doi.org/10.1155/2020/8231752

    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. https://doi.org/10.1007/s00170-018-2167-7

    Article  Google Scholar 

  18. Abdelkader R, Kaddour A, Bendiabdellah A, Derouiche Z (2018) Rolling bearing fault diagnosis based on an improved denoising method using the complete ensemble empirical mode decomposition and the optimized thresholding operation. IEEE Sens J 18(17):7166–7172. https://doi.org/10.1109/JSEN.2018.2853136

    Article  Google Scholar 

  19. 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 

  20. Lei Y, He Z, Zi Y (2009) Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mech Syst Signal Process 23(4):1327–1338. https://doi.org/10.1016/j.ymssp.2008.11.005

    Article  Google Scholar 

  21. Li M, Wang H, Tang G, Yuan H, Yang Y (2014) An improved method based on CEEMD for fault diagnosis of rolling bearing. Adv Mech Eng. https://doi.org/10.1155/2014/676205

    Article  Google Scholar 

  22. Lu Y, Xie R, Liang SY (2020) CEEMD-assisted kernel support vector machines for bearing diagnosis. Int J Adv Manuf Technol 106(7–8):3063–3070. https://doi.org/10.1007/s00170-019-04858-w

    Article  Google Scholar 

  23. Minhas AS, Kankar PK, Kumar N, Singh S (2021) Bearing fault detection and recognition methodology based on weighted multiscale entropy approach. Mech Syst Signal Process 147:107073. https://doi.org/10.1016/j.ymssp.2020.107073

    Article  Google Scholar 

  24. Imaouchen Y, Kedadouche M, Alkama R, Thomas M (2017) A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection. Mech Syst Signal Process 82:103–116. https://doi.org/10.1016/j.ymssp.2016.05.009

    Article  Google Scholar 

  25. Shang H, Li Y, Xu J, Qi B, Yin J (2020) A novel hybrid approach for partial discharge signal detection based on complete ensemble empirical mode decomposition with adaptive noise and approximate entropy. Entropy. https://doi.org/10.3390/E22091039

    Article  MathSciNet  Google Scholar 

  26. Wei Z, Wang Y, He S, Bao J (2017) A novel intelligent method for bearing fault diagnosis based on affinity propagation clustering and adaptive feature selection. Knowl-Based Syst 116:1–12

    Article  Google Scholar 

  27. Guo J, Zhen D, Li H, Shi Z, Gu F, Ball AD (2019) Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method. Meas J Int Meas Confed 139:226–235. https://doi.org/10.1016/j.measurement.2019.02.072

    Article  Google Scholar 

  28. Chegini SN, Bagheri A, Najafi F (2019) Application of a new EWT-based denoising technique in bearing fault diagnosis. Meas J Int Meas Confed 144:275–297. https://doi.org/10.1016/j.measurement.2019.05.049

    Article  Google Scholar 

  29. Yabin M, Chen C, Qiqi S, Jian W, Hongliang L, Darong H (2018) Fault diagnosis of rolling bearing based on EMD combined with HHT envelope and wavelet spectrum transform. In: Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS. pp. 481–485. https://doi.org/10.1109/DDCLS.2018.8516038.

  30. Zhang M, Wei G (2020) An integrated EMD adaptive threshold denoising method for reduction of noise in ECG. PLoS One 15(7):1–30. https://doi.org/10.1371/journal.pone.0235330

    Article  Google Scholar 

  31. Pincus S (1995) Approximate entropy (ApEn) as a complexity measure. Chaos 5(1):110–117. https://doi.org/10.1063/1.166092

    Article  MathSciNet  Google Scholar 

  32. Yan R, Gao RX (2007) Approximate Entropy as a diagnostic tool for machine health monitoring. Mech Syst Signal Process 21(2):824–839. https://doi.org/10.1016/j.ymssp.2006.02.009

    Article  Google Scholar 

  33. Zhang Y, Ji J, Ma B (2020) Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2020.107619

    Article  Google Scholar 

  34. Chen B, Yu S, Yu Y, Guo R (2019) Nonlinear active noise control system based on correlated EMD and Chebyshev filter. Mech Syst Signal Process 130:74–86. https://doi.org/10.1016/j.ymssp.2019.04.059

    Article  Google Scholar 

  35. Bearing Data Center-Case Western Reserve University. https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website

  36. Bechhoefer E (2016) A quick introduction to bearing envelope analysis, MFPT Data. See also URL http://www.mfpt.org/FaultData

Download references

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Prashant Kumar Sahu & Rajiv Nandan Rai

Authors
  1. Prashant Kumar Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajiv Nandan Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

PKS: conceptualization, methodology, coding, writing—original draft, investigation, validation. RNR: data curation, visualization, supervision, writing—review and editing.

Corresponding author

Correspondence to Prashant Kumar Sahu.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

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

Sahu, P.K., Rai, R.N. Fault Diagnosis of Rolling Bearing Based on an Improved Denoising Technique Using Complete Ensemble Empirical Mode Decomposition and Adaptive Thresholding Method. J. Vib. Eng. Technol. 11, 513–535 (2023). https://doi.org/10.1007/s42417-022-00591-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42417-022-00591-z

Keywords

Search

Navigation