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Features of Singular Value Decomposition and Its Application to the Vibration Monitoring of Turboprop Engine

  • Cheng Li
  • Chen Lishun
  • Liang Tao
  • Guo Li
  • Cheng Ming
  • Zeng Lin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

The singular value decomposition (SVD) can decompose an original signal into a series of component signals linearly. By means of analyzing deeply the fundamental principle and existing problems of Hankel matrix-based SVD, This paper reveals the three basic features of SVD, including linear decomposition, reconstruction component frequency domain disorder and band-pass filtering. Based on those features a new SVD method is put forward. Numerical simulation results show that the proposed method not only solve the frequency domain disorder problem of traditional SVD, and can achieve a given linear band-pass filter bandwidth, complete recovery of original signal amplitude, frequency and phase characteristics in any given frequency nearby. Other signal processing methods have no such advantages. The proposed method has been successfully applied to the vibration signal extraction of a certain type of turbofan engine, and the results show that the method has excellent in the feature extraction.

Keywords

Singular value decomposition Band-pass filtering Bandwidth Vibration detection Feature extraction 

References

  1. 1.
    Lv, Z., Zhang, W., Xu, J., et al.: A noise reduction method based singular spectrum and its application in machine fault diagnosis. Chin. J. Mech. Eng. 35(3), 85–88 (1999)Google Scholar
  2. 2.
    Alton, J., Fairley, N.: Noise reduction in X-ray photoelectron spectromicroscopy by a singular value decomposition sorting procedure. J. Electron. Spectrosc. Relat. Phenom. 148(1), 29–40 (2005)CrossRefGoogle Scholar
  3. 3.
    Zhang, B., Li, J.: Denoising method based on Hankel matrix and SVD and its application in flight flutter testing data preprocessing. J. Vib. Shock 28(2), 162–166 (2009)Google Scholar
  4. 4.
    Feng, G., Zhu, Y., Sun, H., et al.: Application of an effective singular value selection method in faint signal feature extraction. Mech. Sci. Technol. Aerosp. Eng. 31(9), 1449–1453 (2012)Google Scholar
  5. 5.
    Shen, Y., Yang, S., Kong, D.: New method of blind source separation in under-determined mixtures based on singular value decomposition and application. J. Mech. Eng. 45(8), 64–70 (2009)CrossRefGoogle Scholar
  6. 6.
    Hasan, D., Gholamreza, A., Cagri, O.: Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci. Remote Sens. Lett. 7(2), 333–337 (2010)CrossRefGoogle Scholar
  7. 7.
    Zhao, X., Ye, B., Chen, T.: Extraction method of faint fault feature based on wavelet-SVD difference spectrum. J. Mech. Eng. 48(7), 37–47 (2012)CrossRefGoogle Scholar
  8. 8.
    Yang, Y., Wei, K., Dong, X.: Time-varying frequency modulated component extraction based on parameterized demodulated and singular value decomposition. IEEE Trans. Instrum. Meas. 65(2), 276–285 (2015)Google Scholar
  9. 9.
    Ding, J., Wang, H., Lin, J.: Detection of dynamic imbalance due to cardan shaft in high-speed train based on EMD-Hankel-SVD method. J. Vib. Shock 34(9), 166–167 (2015)Google Scholar
  10. 10.
    Ding, J., Lin, J., Wang, H., et al.: Detection of the dynamic unbalance with cardan shaft applying the second wavelet transform and singular value decomposition. J. Mech. Eng. 50(12), 110–116 (2014)CrossRefGoogle Scholar
  11. 11.
    Zhao, X., Ye, B., Chen, T.: Difference spectrum theory of singular value and its application to the fault diagnosis of headstock of lathe. J. Mech. Eng. 46(1), 100–108 (2010)CrossRefGoogle Scholar
  12. 12.
    Zhao, X., Ye, B.: Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis. Mech. Syst. Signal Process. 23, 1062–1075 (2009)CrossRefGoogle Scholar
  13. 13.
    Alonso, F.J., Salgado, D.R.: Analysis of the structure of vibration signals for tool wear detection. Mech. Syst. Signal Process. 22(3), 735–748 (2008)CrossRefGoogle Scholar
  14. 14.
    Zhao, X., Nie, Z., Chen, T.: Number law of effective singular values of signal and its application to feature extraction. J. Vib. Eng. 29(3), 532–541 (2016)Google Scholar
  15. 15.
    Cheng, L., Qu, K., Chen, W., et al.: Vibration monitoring threshold of turboprop engine reducer. J. Vib. Shock 34(18), 136–141 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Cheng Li
    • 1
    • 2
  • Chen Lishun
    • 1
  • Liang Tao
    • 1
  • Guo Li
    • 3
  • Cheng Ming
    • 1
    • 4
  • Zeng Lin
    • 1
  1. 1.Aeronautics and Astronautics Engineering CollegeAir Force Engineering UniversityXi’anChina
  2. 2.Advanced Aero Engine Collaborative Innovation CenterBeijingChina
  3. 3.The Chinese People’s Liberation Army 95606 TroopsGuiyangChina
  4. 4.The Chinese People’s Liberation Army 93066 TroopsShenyangChina

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