【作者】 陈祖云；

【导师】 杨胜强；

【作者基本信息】 中国矿业大学 ， 安全技术及工程， 2009， 博士

【摘要】 煤矿瓦斯监测监控系统已基本成形,但是缺乏对事态发展发生的前期预警功能。本文利用煤矿瓦斯监控系统监测的瓦斯浓度数据信息来研究煤与瓦斯突出的发生、发展及其动力学等非线性特征,以及基于支持向量机的煤与瓦斯突出及非突出的预测。主要创新性成果体现在如下几个方面:1)分别从能量和动量守恒的角度,建立了煤与瓦斯突出的尖点突变模型,得出了突出的突变机理和发生条件,从而为煤与瓦斯突出灾害预测与防止提供了新的理论依据。2)分析了煤巷掘进工作面瓦斯涌出量的影响因素。理论和监测表明:工作面瓦斯涌出量动态,与工作面前方的突出危险性存在着较好的一致性,突出前工作面瓦斯涌出量变化异常;而非突出时瓦斯浓度比较均匀且较小。指出了工作面瓦斯涌出量动态特征是一项较好的非接触式预测指标,具有不占用作业时间、不施工预测钻孔,能连续动态预测的优势,能弥补现在的瓦斯监测监控系统不能预测煤与瓦斯突出的缺陷。3)运用混沌动力学理论,研究了突出发生前与不发生突出的对比混沌运动特征。计算了煤巷掘进工作面瓦斯涌出量的Hurst指数,其Hurst指数都小于0.5,表明具有更强的突变性。采用复自相关函数法计算了相空间的延迟时间,发现了突出前瓦斯浓度的相空间延迟时间不小于不发生突出的相空间延迟时间。指出了传统计算关联维方法的缺陷,提出了适用于高维混沌系统的新的关联维算法。发现了突出前瓦斯浓度的关联维都大于不发生突出的;而最小饱和嵌入维却正好相反。改进了考虑到较小演化向量长度及较小的演化角度要求的Wolf算法,发现了突出前瓦斯浓度lyapunov指数的最大值都大于不发生突出的规律。4)指出了局域预测法中的不足是最邻近点与中心点的关联程度,提出了计算最邻近点的新方法,能有效地防止产生伪邻近点。指出了Lyapunov预报模式预测值的正负取舍对瓦斯浓度预报的整体精度有较大影响,提出了Lyapunov预报模式预测值的判定思路,并且也提出了相应的判定算法。提出了基于小波与混沌集成的混沌时间序列的预测方法。本文改进的加权一阶局域预测法、改进的Lyapunov指数模式预测法及基于小波与混沌集成的混沌预测法,并且具有相当高的精度。5)提出了利用支持向量机来识别掘进工作面监测信息中的突出危险性。研究了突出危险性模式识别的核函数构造原理和算法。得出了煤与瓦斯突出及非突出的掘进工作面的监测瓦斯浓度的时域特征向量、频域特征向量、小波域特征向量、分形与混沌特征向量。而且支持向量机能应用在常规煤与瓦斯突出预测和KBD7煤岩动力灾害电磁辐射监测仪的数据处理。结果表明:支持向量机模型识别器解决了小样本、非线性、高维数、局部极小值等实际问题,具有良好的分类识别效果。本文采用Visual C#2005和Matlab7.0语言,开发了煤与瓦斯突出前兆的支持向量机识别系统,与原有的煤矿瓦斯监测监控系统进行数据通信,实现连续非接触式地识别煤与瓦斯突出及非突出,预测结果是可信的,与实际是吻合的,具有重大的现实意义。论文有图113幅,表19个,参考文献161篇。更多还原

【Abstract】 The coal mine monitoring gas system has been established in lots of coal mines, but it is lack the early warning function to forecast coal and gas outburst. In this paper it is to study the development, dynamics, and other non-linear characteristics of the coal and gas outburst occurred by the coal mine monitoring gas system to monitor the gas concentration data, as well as to predict coal and gas outburst based on support vector machines. The main innovation results are as follows:1) Not only the cusp catastrophe models of coal and gas outburst are established respectively from the point of view of energy and momentum conservation, but also the chop mechanisms and the conditions of coal and gas outburst are concluded. So the new theory of disaster prediction and prevention from coal and gas outburst is put forward.2) The effect factors of gas emission from the working face have been analysed. The theory and monitoring data are indicated as follows: The gas emission trends, which are abnormity when the coal and gas outbust happens, while are uniformity and smallness when the coal and gas outbust does not take place from the working face, are consistency with the danger of coal and gas outburst in the working face. The dynamic characteristics of the gas emission from the working face are the better predictors of non-contact, which not only do not engross busywork time, but also do not broach forecast, then which have the advantage of continued and dynamic forecast,as well as make up the disfigurement which the coal mine monitoring gas system does not forecast coal and gas outburst.3) The contrast features of chaotic motion between coal and gas outburst happening and coal and gas outburst not taking place, are studied by using the Chaos Kinetic theory. The Hurst exponent of gas emission from the working face is calculated. Its results are less than 0.5, which are indicated that it has strong chop characteristic of coal and gas outburst. The retardation time of phase space is calculated by using the complex correlative function method. The retardation time of phase space of gas emission from the working face in the coal and gas outburst happened is biger than that of phase space of gas emission from the working face in the coal and gas no outburst. The traditional method defects of calculating the correlation dimension are pointed out, while the new method of calculating the correlation dimension, which is applicable for high-dimensional chaotic system, is put forward. The correlation dimension of gas emission from the working face in the coal and gas outburst happened is biger than that of phase space of gas emission from the working face in the coal and gas no outburst, while the result of the least inbuilt saturation dimension is opposition. The Wolf arithmetic of Lyapunov exponent is ameliorated in view of the less evolutive vector length and angle, and it is found the law that the Lyapunov exponent of gas emission from the working face in the coal and gas outburst happened is greater than that of phase space of gas emission from the working face in no the coal and gas outburst.4) It is pointed that the shortage of part forecast is rested with the correlative degree between the nearest point and the center point, The calculation of the nearest point of new method which can effectively prevent the neighboring pseudo-point, is put forward. The prediction model of Lyapunov , which the predictive value is positive and negative, is impacted on the overall accuracy of forecasting gas concentration. So, not only the Lyapunov of forecasting model to determine the predictive value of ideas, but also of determining the appropriate algorithm are put forward. Wavelet based on the integration of chaos and chaotic time series forecasting methods is also put forward. The very high precisions of gas concentration are forecasted dividably by improving weight of first-order local-region prediction method, by improving prediction model of Lyapunov, and by improving prediction models based on wavelet and chaotic integrated prediction of chaos.5) It is pointed out that the information to monitor the heading face of the danger of coal and gas outburst is identified by support vector machines in the coal mine monitoring gas system. The kernel pattern recognition function and the structure principles of algorithm applying the heading face of the danger of coal and gas outburst are studied. The time-domain characteristics of the vector, the frequency characteristics of the vector, the wavelet domain feature vector, and fractal and chaos characteristics of the vector are educed in the gas concentration of the heading face of the coal and gas outburst and no-outburst by the coal mine monitoring gas system. And support vector machines are in the application of forecasting conventionally coal and gas outburst, and of processing data from disaster KBD7 coal-power electromagnetic radiation monitor. The results show that the support vector machines identifier model, which solves the real problems of the small sample, non-linear, high-dimension, the local minimum value, and so on, has a good classification results. In this paper, by using Visual C# 2005 and Matlab7.0 language, the support vectormachines recognition system, which distinguishes continually non-contactly coal and gas outburst from no outburst by analysing precursor information of coal mine, with the original coal mine gas monitoring system communicating the internet for data, is empoldered. The forecasting results are believable, are consistent with the mine practice, so the support vector machine recognition system is provided with of great practical significance.更多还原