Journal of Failure Analysis and Prevention

, Volume 15, Issue 1, pp 139–151 | Cite as

Fault Feature Extraction of Hydraulic Pump Based on CNC De-noising and HHT

  • Yukui Wang
  • Hongru Li
  • Peng Ye
Technical Article---Peer-Reviewed


The noise in the vibration signal of hydraulic pump seriously affects the extraction of its fault feature. In order to solve this problem, the discrete cosine transform (DCT) de-noising method is studied and the cosine neighboring coefficients (CNC) de-noising method is put forward aiming at the existing problems of DCT de-noising method. Then a novel method for the fault feature extraction of hydraulic pump is proposed based on the combination of CNC de-noising method and Hilbert–Huang transform (HHT). The vibration signal of pump is de-noised with CNC de-noising method and the fault feature is extracted by performing HHT to the output signal. The analysis results of the simulation signal and the actual one demonstrate that the amount of noise exist in signal affects the complexity and the result of HHT operation, and also testify that the proposed CNC de-noising method and the fault feature extraction method have more superior ability than the traditional ones.


DCT CNC HHT Hydraulic pump Fault feature extraction 



This work is supported by the National Natural Science Foundation of China (No. 51275524) and the General Armaments Department Equipment Support Research Project.


  1. 1.
    Shengfa Yuan, Fulei Chu, Support vector machines-based fault diagnosis for turbo-pump rotor [J]. Mech. Syst. Signal Process. 20, 939–952 (2006)CrossRefGoogle Scholar
  2. 2.
    Jiangping Wang, Hu Hongtao, Vibration-based fault diagnosis of pump using fuzzy technique [J]. Measurement 39, 176–185 (2006)CrossRefGoogle Scholar
  3. 3.
    Du Jun, Shaoping Wang, Haiyan Zhang, Layered clustering multi-fault diagnosis for hydraulic piston pump [J]. Mech. Syst. Signal Process. 36, 487–504 (2013)CrossRefGoogle Scholar
  4. 4.
    Zhen Zhao, Mingxing Jia, Fuli Wang et al., Intermittent chaos and sliding window symbol sequence statistics-based early fault diagnosis for hydraulic pump on hydraulic tube tester [J]. Mech. Syst. Signal Process. 23, 1573–1585 (2009)CrossRefGoogle Scholar
  5. 5.
    N.E. Huang, SHEN Z, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proc. R. Soc. Lond. 454(1), 903–955 (1998)CrossRefGoogle Scholar
  6. 6.
    J. Dybała, R. Zimroz, Rolling bearing diagnosing method based on empirical mode decomposition of machine vibration signal [J]. Appl. Acoust. 77, 195–203 (2014)CrossRefGoogle Scholar
  7. 7.
    J. Yan, L. Lu, Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis. Signal Process. (2013). doi: 10.1016/j.sigpro.2013.11.012
  8. 8.
    G. Georgoulas, T. Loutas, C.D. Stylios, V. Kostopoulos, Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition [J]. Mech. Syst. Signal Process. 41, 510–525 (2013)CrossRefGoogle Scholar
  9. 9.
    G.F. Bin, J.J. Gao, X.J. Li et al., Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network [J]. Mech. Syst. Signal Process. 27, 696–711 (2012)CrossRefGoogle Scholar
  10. 10.
    Z.H.E.N.G. Yi, S.U.N. Xiaofeng, C.H.E.N. Jian et al., Extracting pulse signals in measurement while drilling using optimum denoising methods based on the ensemble empirical mode decomposition [J]. Petrol. Explor. Develop 39(6), 798–801 (2012)CrossRefGoogle Scholar
  11. 11.
    Chen Yanlong, Zhang Peilin, Xu Chao et al., Fault diagnosis of rolling bearing based on DCT and EMD [J]. Electron. Meas. Technol. (P.R. China) 32(2), 121–125 (2012). (In Chinese)Google Scholar
  12. 12.
    H. Zang, Q. Li, S. Wang et al., Bearing fault diagnosis based on improved DCT and EMD [J]. Bearing (P.R. China) 3, 53–56 (2013)Google Scholar
  13. 13.
    A. Bouchikhi, A.O. Boudraa, Multi-component AM-FM signals analysis based on EMD B-splines ESA [J]. Signal Process. 92, 2214–2228 (2012)CrossRefGoogle Scholar
  14. 14.
    Li Ji, Pan Mengchun, Tang Ying et al., Analysis and preprocessing of geomagnetic signals based on morphological filter and Hilbert Huang transform [J]. Chin. J. Sci. Instrum. (P. R. China) 33(10), 2175–2180 (2012). (In Chinese)Google Scholar
  15. 15.
    Nuno Roma, Leonel Sousa, A tutorial overview on the properties of the discrete cosine transform for encoded image and video processing [J]. Signal Process. 91, 2443–2464 (2011)CrossRefGoogle Scholar
  16. 16.
    I.Y. Soon, S.N. Koh, C.K. Yeo, Noisy speech enhancement using discrete cosine transform [J]. Speech Commun. 24, 249–257 (1998)CrossRefGoogle Scholar
  17. 17.
    DattatrayV Jadhav, RaghunathS Holambe, Radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition [J]. Signal Process. 88, 2604–2609 (2008)CrossRefGoogle Scholar
  18. 18.
    M.T. Signes, J.M. García, H. Mora, Improvement of the discrete cosine transform calculation by means of a recursive method [J]. Math. Comput. Modell. 50, 750–764 (2009)CrossRefGoogle Scholar
  19. 19.
    Y. Chen, P. Zhang, D. Wu et al., Weak fault signal extrction and identification based on DCT [J]. Noise Vib. Control (P.R. China) 1, 133–136 (2012). (In Chinese)Google Scholar
  20. 20.
    Y. Chen, P. Zhang, B. Li et al., Combined bearing fault diagnosis method based on energy aggregation [J]. Noise Vibr. Control (P.R. China) 1, 191–196 (2013)Google Scholar
  21. 21.
    T.T. Cai, B.W. Silverman, Incorporating information on neighboring coefficients into wavelet estimation [J]. Sankhya Ser. B 63, 127–148 (2001)Google Scholar
  22. 22.
    Y. Yang, Y. Wei, Neighboring coefficients preservation for signal denoising [J]. Circuits Syst. Signal Process. 31, 827–832 (2012)CrossRefGoogle Scholar
  23. 23.
    He Wangpeng, Zi Yanyang, Chen Binqiang et al., Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis [J]. Sci. China 56(8), 1956–1965 (2013)CrossRefGoogle Scholar
  24. 24.
    S. Yang, Z. Zhao, Improved wavelet denoising using neighboring coefficients and its application to machinery fault diagnosis [J]. Chin. J. Mech. Eng. (P.R. China) 49(17), 137–141 (2013). (In Chinese)CrossRefGoogle Scholar
  25. 25.
    Su Wensheng, Wang Fengtao, Zhang Zhixin et al., Application of EMD and spectral kurtosis in early fault diagnosis of rolling element bearings [J]. J. Vibr. Shock (P.R. China) 29(3), 18–21 (2010). (In Chinese)Google Scholar

Copyright information

© ASM International 2014

Authors and Affiliations

  1. 1.Shijiazhuang Mechanical Engineering CollegeShijiazhuangPeople’s Republic of China

Personalised recommendations