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Statistical Characteristics of Pantograph-Catenary Contact Pressure

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Detection and Estimation Research of High-speed Railway Catenary

Part of the book series: Advances in High-speed Rail Technology ((ADVHIGHSPEED))

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Abstract

In order to grasp the basic characteristics of pantograph-catenary interaction and study the influence of parameters of the pantograph and the catenary on each other, the analysis of pantograph-catenary dynamic coupling is very important. The statistical characteristics of pantograph-catenary data are the basis of pantograph-catenary relationship analysis.

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References

  1. Gao Y, Lin JH, Li C (2009) Pretreatment and time-domain analysis of long-wave irregular data of Maglev track. J Comput Appl 29(6):353–355

    Google Scholar 

  2. Xiufang C, Shouhua J, Hualiang Z (2008) PSD analysis on track irregularity of railway line for passenger transport. Eng Sci 10(04): 56–59, 83

    Google Scholar 

  3. Xiubo L, Wu W (2000) PSD analysis of short wave irregularity on welded joints. China Railw Sci 21(02):26–34

    Google Scholar 

  4. Borgant P, Flandrin P (2009) Stationarization via surrogate. J Stat Mech Theory Exp 01:1–13

    Google Scholar 

  5. Borgnat P, Flandrin P, Honeine P et al (2010) Testing stationarity with surrogates: a time-frequency approach. IEEE Trans Signal Process 58(7):3459–3470

    Article  MathSciNet  Google Scholar 

  6. Shuyuan H (2003) Application of time series analysis. Peking University Press, Beijing

    Google Scholar 

  7. Schlitzer G (1995) Testing the stationarity of economic time series: further Monte Carlo evidence. Ricerche Econ 49(95):125–144

    Article  MATH  Google Scholar 

  8. Xiaoxiao Z (2010) The analysis of contact force between the pantograph and contact line in electrified railway via signal processing methods. Southwest Jiaotong University, Chengdu

    Google Scholar 

  9. Yi D, Wang Z (1996) Measurement data modeling and parameter estimation. National University of Defense Technology Press, Changsha

    Google Scholar 

  10. Kalamkar SS, Banerjee A, Roychowdhury A (2012) Malicious user suppression for cooperative spectrum sensing in cognitive radio networks using Dixon’s outlier detection method. In: 2012 national conference on IEEE communications, pp 1–5

    Google Scholar 

  11. Pop S, Ciascai I, Pitica D (2010) Statistical analysis of experimental data obtained from the optical pendulum. In: IEEE 16th international symposium for 2010 design and technology in electronic packaging (SIITME), pp 207–210

    Google Scholar 

  12. Grubbs FE (1950) Sample criteria for testing outlying observations. Ann Math Stat 21(1):27–58

    Article  MathSciNet  MATH  Google Scholar 

  13. Angiulli F, Basta S, Pizzuti C (2006) Distance-based detection and prediction of outliers. IEEE Trans Knowl Data Eng 18(2):145–160

    Article  MATH  Google Scholar 

  14. Li B, Liu X (2010) Study on designed dynamic wheel loads of middle speed and high speed railways in China based on theory of random vibration. J China Railw Soc 32(05):114–118

    Google Scholar 

  15. Ma W, Luo S, Song R (2006) Influence of track irregularity on longitudinal vibration of wheelset and correlation performance. J South Jiaotong Univ 03:238–251

    Google Scholar 

  16. Xie J (2010) Analysis and power spectrum research on catenary irregularities of electric railway. Southwest Jiaotong University, Chengdu

    Google Scholar 

  17. Cho YH, Lee K, Park Y et al (2010) Influence of contact wire pre-sag on the dynamics of pantograph-railway catenary. Int J Mech Sci 52(11):1471–1490

    Article  Google Scholar 

  18. Zhai WM, Cai CB (1998) Effect of locomotive vibrations on pantograph-catenary system dynamics. Veh Syst Dyn 29:47–58

    Article  Google Scholar 

  19. Huang NE, Shen Z, Long SSR et al (1971) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond Ser A 1998(454):903–995

    MathSciNet  MATH  Google Scholar 

  20. Liu Z, Sun W, Zeng J (2014) A new short-term load forecasting method of power system based on EEMD and SS-PSO. Neural Comput Appl 24(3–4):973–983

    Article  Google Scholar 

  21. Huang N, Wu M, Long S et al (2003) A confidence limit for the empirical mode decomposition and Hilbert spectral analysis. Proc R Soc Lond A Math Phys Eng Sci 459(2037):2317–2345

    Google Scholar 

  22. Wu Z, Huang N E (2004) A Study of the characteristics of white noise using the empirical mode decomposition method. Proc R Soc Lond A Math Phys Eng Sci 460(2046):1597–1611

    Google Scholar 

  23. Bao Y, Xie J (2011) Contrast study on power quality detection using EMD and EEMD. In: 2011 international conference on consumer electronics, communications and networks, pp 2074–2077

    Google Scholar 

  24. Dwyer RF (1983) Detection of non-Gaussian signals by frequency domain kurtosis estimation. In: Proceedings of the international conference on acoustics, speech, and signal processing. Boston, MA, USA, pp 607–610

    Google Scholar 

  25. Vrabie V, Granjon P, Servière C (2003) Spectral kurtosis: from definition to application. In: Proceedings of the 6th IEEE international workshop on nonlinear signal and image processing. Grado-Trieste, Italy, pp 1–3

    Google Scholar 

  26. Antoni J (2006) The spectral kurtosis: a useful tool for characterizing non-stationary signals. Mech Syst Signal Process 20(3):282–307

    Article  Google Scholar 

  27. Antoni J (2007) Fast computation of the kurtogram for the detection of transient faults. Mech Syst Signal Process 21(2):108–124

    Article  Google Scholar 

  28. Liu Z, Zhang Q (2014) An approach to recognize the transient disturbances with spectral kurtosis. IEEE Trans Instrum Meas 63(1):46–55

    Article  Google Scholar 

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Correspondence to Zhigang Liu .

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Liu, Z. (2017). Statistical Characteristics of Pantograph-Catenary Contact Pressure. In: Detection and Estimation Research of High-speed Railway Catenary. Advances in High-speed Rail Technology. Springer, Singapore. https://doi.org/10.1007/978-981-10-2753-6_2

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  • DOI: https://doi.org/10.1007/978-981-10-2753-6_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2752-9

  • Online ISBN: 978-981-10-2753-6

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