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Nonlinear Dynamics

, Volume 84, Issue 1, pp 295–310 | Cite as

Multi-mode separation and nonlinear feature extraction of hybrid gear failures in coal cutters using adaptive nonstationary vibration analysis

  • Zhixiong Li
  • Yu Jiang
  • Xuping Wang
  • Z. Peng
Original Paper

Abstract

Reliable condition monitoring and fault diagnosis is an important issue for the normal operation of coal cutter gear systems. Intrinsic deterioration indicators are always hidden in the vibration response of the gearboxes, and it is often very difficult to correctly extract them due to nonlinear/chaotic nature of the vibration signal. Literature review suggests that hybrid gear faults diagnosis is a challenging task and how to extract quantitative indicators for hybrid faults detection is attracting considerable attentions. In order to address this issue, a new adaptive nonstationary vibration analysis method is proposed in this paper to extract useful quantitative indicators for hybrid gear faults decoupling detection. In this new technology, the center frequencies of the narrow bands of intrinsic modes contained in the vibration signal were adaptively estimated by the variational model decomposition (VMD) to determine the bandwidth of the modes. Hence, the hybrid gear faults were decoupled into single faults in the form of VMD modes. Then, the time and frequency features of each mode were calculated to obtain the feature space. Lastly, the feature space was projected into the reproducing kernel Hilbert space by the spectral regression-optimized kernel Fisher discrimination (SRKFD), where the instinct nonlinear structure in the original data can be identified and thus useful quantitative indicators can be extracted. Reliable hybrid faults decoupling detection was then achieved. Specially designed numerical simulations and experiments were conducted to evaluate the proposed VMD-SRKFD method on hybrid gear faults diagnosis of coal cutters. The performance was compared with existing techniques. The analysis results show high performance of the proposed method on quantitative hybrid faults detection in the coal cutter gear system.

Keywords

Coal cutters Gear transmission systems Nonlinear vibration Hybrid faults Fault decoupling 

Notes

Acknowledgments

This research was funded by the National Natural Sciences Foundation of China (NSFC) (No. 51505475), the Fundamental Research Funds for the Central Universities (No. 2015XKMS018) and the Priority Academic Program Development of Jiangsu Higher Education Institutions.

References

  1. 1.
    Jiang, Y., Zhu, H., Li, Z., Peng, Z.: The nonlinear dynamics response of cracked gear system in a coal cutter taking environmental multi-frequency excitation forces into consideration. Nonlinear Dyn. (2015). doi: 10.1007/s11071-015-2409-2
  2. 2.
    Qian, P.: Fault diagnosis and reliability analysis for transmission system of shearer cutting part. Ph. D Thesis, China University of Mining and Technology, Xuzhou, China (2015)Google Scholar
  3. 3.
    Li, Z., Ge, S., Zhu, H.: Key issues in the wear fault monitoring and diagnosis for critical components of coal cutters under deep coal seam. Tribology 34(6), 729–730 (2014)Google Scholar
  4. 4.
    Widodo, A., Yang, B.: Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 21, 2560–2574 (2007)CrossRefGoogle Scholar
  5. 5.
    Randall, B.: Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications. Wiley, New York (2011)CrossRefGoogle Scholar
  6. 6.
    Jardine, A., Lin, D., Banjevic, D.: A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 20, 1483–1510 (2006)CrossRefGoogle Scholar
  7. 7.
    Shen, R., Zheng, H., Jin, H., Kang, H., Zhang, J.: Application of max-min ant system and rough sets to compound fault diagnosis of bearing. J. Vib. Meas. Diagn. 30, 128–131 (2010)Google Scholar
  8. 8.
    Luo, Z., He, X., Xu, A., Chen, Q., Chen, P.: Application of possibility theory in rolling bearing compound fault diagnosis. J. Vib. Shock 30, 73–76 (2011)Google Scholar
  9. 9.
    Yuan, J., He, Z., Zi, Y.: Separation and extraction of electromechanical equipment compound faults using lifting multiwavelets. J. Mech. Eng. 46, 79–85 (2010)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., He, Z., Zi, Y.: Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech. Syst. Signal Process. 24, 119–137 (2010)CrossRefGoogle Scholar
  11. 11.
    Li, R., Yu, D., Chen, X., Liu, J.: A compound fault diagnosis method for gearboxes based on chirplet path pursuit and EEMD. J. Vib. Shock 33, 51–56 (2014)Google Scholar
  12. 12.
    Li, R., Yu, D., Chen, X., Liu, J.: A compound fault diagnosis method for gearbox based on order tracking and cyclostationary demodulation. China Mech. Eng. 24, 1320–1327 (2013)Google Scholar
  13. 13.
    Li, Z.: A novel solution for the coupled faults isolation in gear pairs using the conception of frequency tracking. Electr. Electron. Eng. 20, 69–72 (2014)Google Scholar
  14. 14.
    Li, H., Zheng, H., Tang, L.: Application of morphological component analysis to gearbox compound fault diagnosis. J. Vib. Meas Diagn. 33, 620–626 (2013)Google Scholar
  15. 15.
    Li, Z., Peng. Z: A new nonlinear blind source separation method with chaos indicators for decoupling diagnosis of hybrid failures: A marine propulsion gearbox case with a large speed variation. Chaos Solitons Fractals (2015). doi: 10.1016/j.chaos.2015.09.023
  16. 16.
    Wang, Y., Xu, G., Zhang, Q., Liu, D., Jiang, K.: Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions. J. Sound Vib. 348, 381–396 (2015)CrossRefGoogle Scholar
  17. 17.
    Jiang, Y., Hua, Z., Li, Z.: A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator. Chaos Solitons Fractals (2015). doi: 10.1016/j.chaos.2015.09.007
  18. 18.
    Huang, N., Shen, Z., Long, S., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. A Math. Phys. Eng. Sci. 454, 903–995 (1998)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Proc. 62, 531–544 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Hou, T., Shi, Z.: Adaptive data analysis via sparse time-frequency representation. Adv. Adapt. Data Anal. 3, 1–28 (2011)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Feldman, M.: Time-varying vibration decomposition and analysis based on the Hilbert transform. J. Sound Vib. 295, 518–530 (2006)CrossRefMATHGoogle Scholar
  22. 22.
    Jiang, Y., Wu, J., Zong, C.: An effective diagnosis method for single and multiply defects detection in gearbox based on nonlinear feature selection and kernel-based extreme learning machine. J. Vibro Eng. 16, 499–512 (2014)Google Scholar
  23. 23.
    Cai, D., He, X., Han, J., Huang, T.: Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548–1560 (2011)CrossRefGoogle Scholar
  24. 24.
    Cai, D., He, X., Han, J.: Speed up kernel discriminant analysis. VLDB J. 20, 21–33 (2011)CrossRefGoogle Scholar
  25. 25.
    Li, P., Bu, J., Yang, Y., Ji, R., Chen, C., Cai, D.: Discriminative orthogonal nonnegative matrix factorization with flexibility for data representation. Expert Syst. Appl. 41, 1283–1293 (2014)CrossRefGoogle Scholar
  26. 26.
    Lei, Y., Zuo, M., He, Z., Zi, Y.: A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Syst. Appl. 37, 1419–1430 (2010)CrossRefGoogle Scholar
  27. 27.
    Falconer, K.: Fractal Geometry. Wiley, New York (2003)CrossRefMATHGoogle Scholar
  28. 28.
    Manolakis, D., Ingle, V.: Applied Digital Signal Processing. Cambridge University Press, Cambridge (2011)CrossRefMATHGoogle Scholar
  29. 29.
    Li, Z., Yan, X., Yuan, C., Peng, Z., Li, L.: Virtual prototype and experimental research gear multifault diagnosis using wavelet-autoregressive model and principal component analysis method. Mech. Syst. Signal Process. 25(7), 2589–2607 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.School of Mechatronic Engineering, Jiangsu Key Laboratory of Mine Mechanical and Electrical EquipmentChina University of Mining and TechnologyXuzhou People’s Republic of China
  2. 2.Xi’an Research Institute of Hi-TechXi’anPeople’s Republic of China
  3. 3.School of Mechanical and Manufacturing EngineeringThe University of New South WalesSydneyAustralia

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