Skip to main content
SpringerLink
Log in

Research on fault diagnosis of planetary gearbox based on variable multi-scale morphological filtering and improved symbol dynamic entropy

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

Abstract

Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of variable multi-scale morphological filtering (VMSMF) and average multi-scale double symbolic dynamic entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal-scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the turn domain resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.

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

Similar content being viewed by others

Data availability

The data used in this paper are all owned by the lab of the research group. As the research is still continuing, the data involved in this paper is not publicly available.

Code availability

The algorithm involved in this paper is still being studied by the research group, so it is not publicly disclosed.

References

  1. Cui LL, Sun Y, Wang X, Wang HQ (2021) Spectrum-based, full-band pre-processing and two-dimensional separation of bearing and gear compound faults diagnosis. IEEE Trans Instrum Meas 70:3513216

    Article  Google Scholar 

  2. Li YB, Feng K, Liang XH, Zuo MJ (2019) A fault diagnosis method for planetary gearboxes under non-stationary working conditions using improved Vold-Kalman filter and multi-scale sample entropy. J Sound Vib 439:271–286

    Article  Google Scholar 

  3. Li JL, Wang HQ, Song LY (2021) A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT. Chin J Aeronaut 34(7):157–169

    Article  Google Scholar 

  4. Zhang M, Jiang Z, Feng K (2017) Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump. Mech Syst Signal Process 93:460–493

    Article  Google Scholar 

  5. Ren XP, Li P, Wang CG, Zhang C (2018) Rolling bearing early fault diagnosis based on improved VMD and envelope derivative operator. J Vib Shock 37(15):6–13

    Google Scholar 

  6. Zhang ZL, Cheng Q, Qi BB, Tao ZQ (2021) A general approach for the machining quality evaluation of S-shaped specimen based on POS-SQP algorithm and Monte Carlo method. J Manuf Syst 60:553–568

    Article  Google Scholar 

  7. Niu P, Cheng Q, Liu ZF, Chu HY (2021) A machining accuracy improvement approach for a horizontal machining center based on analysis of geometric error characteristics. Int J Adv Manuf Technol 112(9–10):2873–2887

    Article  Google Scholar 

  8. Serra JP (1982) Image analysis and mathematical morphology. Biometrics 39(2):536

    Google Scholar 

  9. Nikolaou NG, Antoniadis IA (2003) Application of morphological operators as envelope extractors for impulsive-type periodic signals. Mech Syst Signal Process 17(6):1147–1162

    Article  Google Scholar 

  10. Wang J, Xu G, Zhang Q et al (2009) Application of improved morphological filter to the extraction of impulsive attenuation signals. Mech Syst Signal Process 23(1):236–245

    Article  Google Scholar 

  11. Shen CQ, Xie WD, Zhu ZK (2013) Rolling element bearing fault diagnosis based on EEMD and improved morphological filtering method. J Vib Shock 32(2):39–43

    Google Scholar 

  12. Jiang W, Zheng Z, Zhu Y et al (2015) Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis. Mech Syst Signal Process 58–59:179–205

    Article  Google Scholar 

  13. Li B, Zhang PL, Wang ZJ et al (2011) Gear fault detection using multi-scale morphological filters. Measurement 44(10):2078–2089

    Article  Google Scholar 

  14. Chen Q, Chen Z, Sun W et al (2015) A new structuring element for multi-scale morphology analysis and its application in rolling element bearing fault diagnosis. J Vib Control 21(4):765–789

    Article  Google Scholar 

  15. Wang PX, Song LY, Guo XD, Wang HQ, Cui L (2021) A High-stability diagnosis model based on a multiscale feature fusion convolutional neural network. IEEE Trans Instrum Meas 70:709

    Google Scholar 

  16. Liu TT, Cui LL, Zhang C (2021) Study on fault diagnosis method of planetary gearbox based on turn domain resampling and variable multi-scale morphological filtering. Symmetry 52(13):1–17

    Google Scholar 

  17. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 5(1):1–53

    Google Scholar 

  18. Kolmogorov AN (1958) A new metric invariant of transient dynamical systems and auto-morphisms in Lebesgue spaces. Dokl Akad Nauk SSSR 951(5):861–864

    MATH  Google Scholar 

  19. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci 88(6):2297–2301

    Article  MATH  Google Scholar 

  20. Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. AJP Heart Circ Physiol 278(6):H2039–H2049

    Article  Google Scholar 

  21. Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102

    Article  Google Scholar 

  22. Daw CS, Finney CE, Tracy ER (2003) A review of symbolic analysis of experimental data. Rev Sci Instrum 74(2):915–930

    Article  Google Scholar 

  23. Hao BL (1991) Symbolic dynamics and characterization of complexity. Physica D 51(1–3):161–176

    Article  MATH  Google Scholar 

  24. Zheng WM, Hao BL (1996) Applied symbolic dynamics. Prog Phys 10(3):316–373

    Google Scholar 

  25. Kurths J, Voss A, Sapatin P et al (1995) Quantitative analysis of heart rate variability. Chao 5:88–94

    Article  Google Scholar 

  26. Zhang H, Zeng WT, Yan W (2017) Fault diagnosis of hydraulic pump based on symbolic dynamics entropy and SVM. J Vib Meas Diagn 32(4):288–293

    Google Scholar 

  27. Xue HT, Ding DY, Zhang ZM, Wu M, Wang HQ (2021) A fuzzy system of operation safety assessment using multi-model linkage and multi-stage collaboration for in-wheel motor. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2021.3052092

    Article  Google Scholar 

  28. Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy to distinguish physiologic and synthetic RR time series. Comput Cardiol 29(29):137–140

    Article  Google Scholar 

  29. Ding C, Feng FZ, Zhang BZ, Wu SJ (2020) MMSDE and its application in feature extraction of a planetary gearbox. J Vib Shock 39(13):97–102

    Google Scholar 

  30. Li YB, Yang YT, Li GY et al (2017) A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection. Mech Syst Signal Process 91:295–312

    Article  Google Scholar 

  31. Cheng Q, Qi BB, Liu ZF, Zhang CX, Xue DY (2019) An accuracy degradation analysis of ball screw mechanism considering time-varying motion and loading working conditions. Mech Mach Theory 134:1–23

    Article  Google Scholar 

  32. Wang X, Cui LL, Wang HQ, Jiang H (2021) A generalized health indicator for performance degradation assessment of rolling element bearings based on graph spectrum reconstruction and spectrum characterization. Measurement 176:109165

    Article  Google Scholar 

  33. Zhao HM, Liu HD, Xu JJ (2020) Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine. IEEE Trans Instrum Meas 69(7):4165–4172

    Article  Google Scholar 

  34. Guo JC, Zhen D, Li HY (2020) Fault detection for planetary gearbox based on an enhanced average filter and modulation signal bispectrum analysis. ISA Trans 101:408–420

    Article  Google Scholar 

  35. Maragos P (2013) Representations for morphological image operators and analogies with linear operators. Adv Imaging Electron Phys 177:45–187

    Article  Google Scholar 

  36. Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett 92(8):705–708

    Google Scholar 

  37. Luo YC, Cui LL, Zhang JY, Ma JF (2021) Vibration mechanism and improved phenomenological model of the planetary gearbox with broken ring gear fault. J Mech Sci Technol 35(5):1867–1879

    Article  Google Scholar 

  38. Luo YC, Cui LL, Zhang JY, Ma JF (2021) Vibration mechanism and improved phenomenological model of planetary gearbox with broken sun gear fault. Measurement 178:109356

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the support of the National Natural Science Foundation of China (Grant No. 52075008) and the Key Laboratory of Advanced Manufacturing Technology. Finally, the authors would like to thank the editors and reviewers for their valuable comments and constructive suggestions.

Funding

This research is funded by the National Natural Science Foundation of China (Grant Nos. 52075008).

Author information

Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District, Beijing, 100124, China

    Tongtong Liu, Lingli Cui & Jianyu Zhang

  2. Inner Mongolia Key Laboratory of Intelligent Diagnosis and Control of Mechatronic Systems, Inner Mongolia University of Science and Technology, Baotou, 014010, China

    Tongtong Liu & Chao Zhang

Authors
  1. Tongtong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingli Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LC and TL conceived and designed the experiments. JZ and TL performed the experiments. TL and CZ analyzed the data; LC and JZ provided guidance and recommendations for research; TL contributed to the contents and writing of the manuscript. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Lingli Cui.

Ethics declarations

Ethical approval

The team affirmed that all the research was original research of the research group without any bad behavior.

Consent to participate

The methods involved in this paper are agreed to be shared.

Consent for publication

The content of this thesis is agreed to be published in the journal.

Conflict of interest

The author declares no competing interests.

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

Liu, T., Cui, L., Zhang, J. et al. Research on fault diagnosis of planetary gearbox based on variable multi-scale morphological filtering and improved symbol dynamic entropy. Int J Adv Manuf Technol 124, 3947–3961 (2023). https://doi.org/10.1007/s00170-021-08085-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-08085-0

Keywords

Search

Navigation