Skip to main content
SpringerLink
Log in

Time and frequency domain scanning fault diagnosis method based on spectral negentropy and its application

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

Abstract

Rolling bearings are one of the most important components in rotating machinery. It is important to accurately determine the center frequency and bandwidth of the resonant frequency band for bearing fault diagnosis. There are two problems with the existing methods for extracting bearing fault characteristics. First, due to the unreasonable spectrum segmentation, the determined resonant frequency band contains only partial fault information or hidden irrelevant information. Finally, because of the interference of the accidental impact, the correct fault characteristic information cannot be extracted. To solve the above problems, a time-frequency domain scanning empirical spectral negentropy method (T-FSESNE) based on spectral negentropy (NE) and empirical wavelet transform (EWT) is proposed in this paper. The signal is filtered twice by EWT filter: Firstly, the central frequencies of all resonance side bands are determined by using frequency-domain spectral negentropy, and then the optimal bandwidth of the resonance side bands is determined by using time-domain spectral negentropy. According to the determined center frequency and bandwidth, each component is extracted and analyzed by envelope spectrum to realize bearing fault diagnosis. The validity of the extracted methods is verified by bearing fault simulation and experimental signals. The results show that not only the interference of accidental impact can be effectively avoided but also the optimal center frequency and bandwidth can be determined quickly and accurately. More importantly, this method can determine the position of multiple resonance sidebands, which is more suitable for the analysis of complex fault vibration signals in rolling bearings.

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

Similar content being viewed by others

Abbreviations

T-FSESNE:

Time-frequency domain scanning empirical spectral negentropy method

NE:

Spectral negentropy

EWT:

Empirical wavelet transform

FK:

Fast kurtogram

STFT:

Short-time Fourier transform

SNR:

Signal-to-noise

CK:

Correlation kurtosis

ASR:

Adaptive stochastic resonance

WPT:

Wavelet packet transform

DTCWT:

The maximum overlapping discrete wavelet packet transform

ESSK:

Empirical scanning spectrum kurtosis

SE:

Square envelope

SES:

Square envelope spectrum

TSNE:

Time-domain spectrum negentropy

B w :

Fixed bandwidth

B wmin :

Minimum scanning bandwidth

B wmax :

Maximum scan bandwidth

∆f :

Scanning step

∆bw :

The change of the scan bandwidth

References

  1. Zheng J, Pan H, Cheng J (2017) Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing, 85:746–759

  2. Feng GJ et al (2015) Implementation of envelope analysis on a wireless condition monitoring system for bearing fault diagnosis. Int J Autom Comput 12(1):14–24

    Article  Google Scholar 

  3. Stavropoulos P, Papacharalampopoulos A, Vasiliadis E, Chryssolouris G (2016) Tool wear predictability estimation in milling based on multi-sensorial data. Int J Adv Manuf Technol 82(1–4):509–521

    Article  Google Scholar 

  4. Barszcz T, JabŁoński A (2011) A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram. Mech Syst Signal Process 25(1):431–451

    Article  Google Scholar 

  5. Samanta B, Al-Balushi KR (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process 17(2):317–328

    Article  Google Scholar 

  6. Wang D, Tse PW, Tsui KL (2013) An enhanced kurtogram method for fault diagnosis of rolling element bearings. Mech Syst Signal Process 35(1–2):176–199

    Article  Google Scholar 

  7. Chen BQ, Zhang ZS, Zi YY et al (2013) Detecting of transient vibration signatures using an improved fast spatial–spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery. Mech Syst Signal Process 40(1):1–37

    Article  Google Scholar 

  8. Dwyer R (1984) Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals. IEEE J Ocean Eng 9(2):85–92

    Article  Google Scholar 

  9. Ottonello C, Pagnan S (2002) Modified frequency domain kurtosis for signal processing. Electron Lett 30(14):1117–1118

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Sawalhi N, Randall RB, Endo H (2007) The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mech Syst Signal Process 21(6):2616–2633

    Article  Google Scholar 

  13. Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(1):108–124

    Article  Google Scholar 

  14. McDonald GL, Zhao Q, Zuo MJ (2012) Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection. Mech Syst Signal Process 33(Complete):237–255

    Article  Google Scholar 

  15. Wang Y, Xu G, Liang L, Jiang K (2015) Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech Syst Signal Process 54-55:259–276

    Article  Google Scholar 

  16. Lei Y, Han D, Lin J, He Z (2013) Planetary gearbox fault diagnosis using an adaptive stochastic resonance method. Mech Syst Signal Process 38(1):113–124

    Article  Google Scholar 

  17. Antoni J (2016) The infogram: entropic evidence of the signature of repetitive transients. Mech Syst Signal Process 74:73–94

    Article  Google Scholar 

  18. Rother A, Jelali M, Söffker D (2015) A brief review and a first application of time-frequency-based analysis methods for monitoring of strip rolling mills. J Process Control 35:65–79

    Article  Google Scholar 

  19. Lei Y, Lin J, He Z et al (2011) Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mech Syst Signal Process 25(5):1738–1749

    Article  Google Scholar 

  20. Xinghui Z, Jianshe K, Lei X et al (2015) A new improved kurtogram and its application to bearing fault diagnosis. Shock Vib 2015:1–22

    Google Scholar 

  21. Wang Y, He Z, Zi Y (2010) Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech Syst Signal Process 24(1):119–137

    Article  Google Scholar 

  22. Moshrefzadeh A, Fasana A (2018) The autogram: an effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis. Mech Syst Signal Process 105:294–318

    Article  Google Scholar 

  23. Tse PW, Wang D (2013) The design of a new sparsogram for fast bearing fault diagnosis: part 1 of the two related manuscripts that have a joint title as “two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – parts 1 and 2”. Mech Syst Signal Process 40(2):499–519

    Article  Google Scholar 

  24. Tse PW, Wang D (2013) The automatic selection of an optimal wavelet filter and its enhancement by the new sparsogram for bearing fault detection: part 2 of the two related manuscripts that have a joint title as “two automatic vibration-based fault diagnostic methods using the novel sparsity measurement—parts 1 and 2”. Mech Syst Signal Process 40(2):520–544

    Article  Google Scholar 

  25. Gilles J (2013) Empirical wavelet transform. IEEE Trans Signal Process 61(16):3999–4010

    Article  MATH  MathSciNet  Google Scholar 

  26. Yonggang X, Weikang T et al (2019) Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis. Meas Sci Technol 30:1–19

    Google Scholar 

  27. Cao H, Fan F, Zhou K, He Z (2016) Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 82:439–449

    Article  Google Scholar 

  28. Jiang Y, Zhu H, Li Z (2016) A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator. Chaos, Solitons Fractals 89:8–19

    Article  Google Scholar 

  29. Xia JZ et al (2018) Application of resonance demodulation in rolling bearing fault feature extraction based on infogram. J Vib Shock 37(12):29–34

  30. Antoni J, Bonnardot F et al (2004) Cyclostationary modelling of rotating machine vibration signals. Mech Syst Signal Process 18(6):1285–1314

    Article  Google Scholar 

  31. Randall RB, Antoni J et al (2001) The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mech Syst Signal Process 15(5):945–962

    Article  Google Scholar 

  32. Li X (2014) Research on signal detection technology of rolling bearing compound failure. Harbin Institute of Technology

Download references

Funding

This research is financially supported by the National Natural Science Foundation of China (No. 51775005 and No. 51675009) and the Key Laboratory of Advanced Manufacturing Technology. The authors are very grateful for their support.

Author information

Authors and Affiliations

  1. The Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, 100124, China

    Yonggang Xu, Shuang Li, Weikang Tian & Kun Zhang

  2. Harbin Institute of Technology, Harbin, 150080, Heilongjiang, China

    Jun Yu

Authors
  1. Yonggang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weikang Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yonggang Xu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Xu, Y., Li, S., Tian, W. et al. Time and frequency domain scanning fault diagnosis method based on spectral negentropy and its application. Int J Adv Manuf Technol 108, 1249–1264 (2020). https://doi.org/10.1007/s00170-020-05302-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05302-0

Keywords

Search

Navigation