Advertisement

Nonlinear Dynamics

, Volume 93, Issue 4, pp 2517–2531 | Cite as

Operation conditions monitoring of flood discharge structure based on variance dedication rate and permutation entropy

  • Jianwei Zhang
  • Ge Hou
  • Kelei Cao
  • Bin Ma
Original Paper
  • 115 Downloads

Abstract

There has been a growing concern on how to monitor the operation conditions of flood discharge structure in recent decades. However, the online monitoring process is always interfered by ambient excitation which leads to inaccurate and uncertain structural characteristic evaluation. To mitigate the interference, a valid operation conditions monitoring method based on variance dedication rate (VDR) and permutation entropy (VDR-PE) is proposed. Firstly, a de-noising method combining wavelet threshold and empirical mode decomposition is used to remove heavy background noises, reducing the interference of ambient excitation to structural characteristic information. Then VDR method is used to realize the dynamic fusion of multi-channel vibration signals, extracting the vibration characteristic of the overall structure in an accurate and comprehensive way. Finally, permutation entropy is used to extract the entropy value of the fused signal. Through evaluating the operation conditions with coefficient of variation, the online monitoring of flood discharge structure can be realized. The effectiveness of permutation entropy algorithm on signal dynamic monitoring is validated by a simulation experiment. Furthermore, VDR-PE method is applied to Three Gorges dam to compare differences between analytical simulation and finite element simulation. The comparison results show that VDR-PE method can be applied to detect the dynamic changes and reveal the vibration characteristic of the overall structure accurately, which provides a new direction for the online monitoring of flood discharge structure.

Keywords

Flood discharge structure Variance dedication rate Permutation entropy Online monitoring Data fusion 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51679091), the State Key Laboratory of Hydraulic Engineering Simulation and Safety of Tianjin University (Grant No. HESS-1312) and the Program for Science & Technology Innovation Talents in Universities of Henan Province (Grant No.18HASTIT012).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest in preparing this article.

References

  1. 1.
    Zhang, J.W., Jiang, Q., Ma, B., Zhao, Y., Zhu, L.H.: Signal de-noising method for vibration signal of flood discharge structure based on combined wavelet and EMD. J. Vib. Control 23, 2401–2417 (2017)CrossRefGoogle Scholar
  2. 2.
    Su, H.Z., Chen, Z.X., Wen, Z.P.: Performance improvement method of support vector machine-based model monitoring dam safety. Struct. Control Health 23, 252–266 (2016)CrossRefGoogle Scholar
  3. 3.
    Shi, J.J., Liang, M., Guan, Y.P.: Bearing fault diagnosis under variable rotational speed via the joint application of windowed fractal dimension transform and generalized demodulation: a method free from prefiltering and resampling. Mech. Syst. Signal Process. 68, 15–33 (2016)CrossRefGoogle Scholar
  4. 4.
    Ubeyli, E.D.: Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals. Expert Syst. Appl. 37, 1192–1199 (2010)CrossRefGoogle Scholar
  5. 5.
    He, Y.Y., Huang, J., Zhang, B.: Approximate entropy as a nonlinear feature parameter for fault diagnosis in rotating machinery. Meas. Sci. Technol. 23, 045603 (2012)CrossRefGoogle Scholar
  6. 6.
    Widodo, A., Shim, M.C., Caesarendra, W., Yang, B.S.: Intelligent prognostics for battery health monitoring based on sample entropy. Expert Syst. Appl. 38, 11763–11769 (2011)CrossRefGoogle Scholar
  7. 7.
    Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. 88, 2297–2301 (1991)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Zhang, L., Xiong, G.L., Liu, H.S., Zou, H.J., Guo, W.Z.: Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference. Expert Syst. Appl. 37, 6077–6085 (2010)CrossRefGoogle Scholar
  9. 9.
    Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)CrossRefGoogle Scholar
  10. 10.
    Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88, 17–29 (2002)CrossRefGoogle Scholar
  11. 11.
    Zhang, X.Y., Liang, Y.T., Zhou, J.Z., Zang, Y.: A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 69, 164–179 (2015)CrossRefGoogle Scholar
  12. 12.
    Vakharia, V., Gupta, V.K., Kankar, P.K.: A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings. J. Vib. Control 21, 3123–3131 (2015)CrossRefGoogle Scholar
  13. 13.
    Ferlazzo, E., Mammone, N., Cianci, V., Gasparini, S.: Permutation entropy of scalp EEG: a tool to investigate epilepsies: suggestions from absence epilepsies. Clin. Neurophysiol. 125, 13–20 (2014)CrossRefGoogle Scholar
  14. 14.
    Sun, X.L., Zou, Y., Nikiforova, V., Kurths, J., Walther, D.: The complexity of gene expression dynamics revealed by permutation entropy. BMC Bioinform. 11, 607–621 (2010)CrossRefGoogle Scholar
  15. 15.
    Tiwari, R., Gupta, V.K., Kankar, P.K.: Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. J. Vib. Control 21, 461–467 (2015)CrossRefGoogle Scholar
  16. 16.
    Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 39, 202–209 (2012)CrossRefGoogle Scholar
  17. 17.
    Rao, G.Q., Feng, F.Z., Si, A.W., Xie, J.L.: Method for optimal determination of parameters in permutation entropy algorithm. J. Vib. Shock 33, 188–193 (2014)Google Scholar
  18. 18.
    Du, L.C., Song, W.H., Guo, W., Mei, D.C.: Multiple current reversals and giant vibrational resonance in a high-frequency modulated periodic device. EPL 115, 40008 (2016)CrossRefGoogle Scholar
  19. 19.
    Dang, J., He, Y.Y., Jia, R., Dong, K.S., Xie, Y.T.: Detection for non-stationary vibration signal and fault diagnosis of hydropower unit. J. Hydraul. Eng. 47, 173–179 (2016)Google Scholar
  20. 20.
    Gao, J.B., Cai, H.Q.: On the structures and quantification of recurrence plots. Phys. Lett. A 270, 75–87 (2000)CrossRefGoogle Scholar
  21. 21.
    Brown, C.E.: Coefficient of variation. In: Brown, C.E. (ed.) Applied multivariate statistics in geohydrology and related sciences, pp. 155–157. Springer, Berlin (1998)CrossRefGoogle Scholar
  22. 22.
    Singh, H.P., Pal, S.K.: Estimation of population variance using known coefficient of variation of an auxiliary variable in sample surveys. J. Stat. Manag. Syst. 20, 91–111 (2017)CrossRefGoogle Scholar
  23. 23.
    Jamesahar, E., Ghalambaz, M., Chamkha, A.J.: Fluid-solid interaction in natural convection heat transfer in a square cavity with a perfectly thermal-conductive flexible diagonal partition. Int. J. Heat Mass Transf. 100, 303–319 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Water ResourcesNorth China University of Water Resources and Electric PowerZhengzhouChina
  2. 2.State Key Laboratory of Hydraulic Engineering Simulation and SafetyTianjin UniversityTianjinChina

Personalised recommendations