Chaotic Characteristics of Workface Gas Emission Time-Series Data

Abstract:

The chaotic time series phase space reconstruction theory based in this paper. First, the appropriate embedding dimension and delay time are selected by minimum entropy rate. Followed the chaotic behavior are analyzed by the use of the Poincare section map and Power spectrum of time series from the qualitative point of view. Based on NLSR LLE the quantitative study of the chaotic time series characteristics indicators is proposed. Finally, the gas emission workface of Hebi 10th Mine Coal is studied. The several analytical results of the above methods show that: the gas emission time-series data of this workface has chaotic characteristics.

• Periodical: Applied Mechanics and Materials (Volume 340)
• Pages: 456-460
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.340.456
• Online since: July 2013
• Authors: Mei Ying Qiao, Jian Yi Lan
• Keywords: Chaotic Characteristics, Gas Emission, LLE, Time-Series

References

[1] C. Zhuyun. Research of non-linear prediction and identification omen of coal and gas outburst by support vector machines [D], China University Mining and Technology, (2009).

[2] C. Jian, Bai Jingyi , QIAN Jiansheng, Short-Term Forecasting Method of coalmine Gas Concentration Based on Chaotic Time Series[J], Journal of China University of Mining & Technology2008, 27(02): 231~235.

[3] S. Shiliang, SONG Yi1, HE Liwen. Research on determ ination of chaotic characteristics of gas gush based on time series in excavation working face of coal mine[J]. Journal of China Coal Society. 2006, 31(6): 701~705.

[4] F. Takens. Detecting strange attractors in turbulence. [J]. Dynamical Systems and Turbulence, 1981, 898: 366~381.

DOI: 10.1007/bfb0091924

[5] Q. Meiying, Ma Xiaoping, Lan Jianyi. Time Series Short Term Gas Prediction Based on Weighted LS-SVM [J]. Journal o f Mining & Safet y Engineering, 2011, 28 (2): 310~314.

[6] W. Haiyan, Lu San. Nonlinear time series analysis and application[M]. Beijing: Science press. (2006).

[7] L. Cao. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D: Nonlinear Phenomena, 1997, 110(1-2): 43~50.

DOI: 10.1016/s0167-2789(97)00118-8

[8] T. Gautama, Mandic D P, Van Hulle M M. A differential entropy based method for determining the optimal embedding parameters of a signal[C]. Hong Kong, Hong kong: (2003).

DOI: 10.1109/icassp.2003.1201610

[9] L. Shida, Liang Fuming, Liu Shishi. Chaos and Fractal of Natural Science[M]. Beijing: Beijing University Press. (2003).

[10] H. Min. Chaotic Time Series Prediction Theory and Method [M]. Beijing: China Water Power Press. (2007).

[11] J. Argyris, Andreadis I, Pavlos G, et al. On the influence of noise on the largest Lyapunov exponent and on the geometric structure of attractors[J]. Chaos, solitons and fractals, 1998, 9(6): 947~958.

DOI: 10.1016/s0960-0779(97)00145-8

[12] J. Argyris, I. Andreadis. On the influence of noise on the largest Lyapunov exponent of attractors of stochastic dynamic systems[J]. Chaos, solitons and fractals, 1998, 9(6): 959~963.

DOI: 10.1016/s0960-0779(97)00146-x

[13] H. Liu, Z. Dai , W. Li , et al. Noise robust estimates of the largest Lyapunov exponent[J]. Physics Letters A, 2005, 341(1-4): 119~127.

DOI: 10.1016/j.physleta.2005.04.048