【作者】 黄为勇；

【导师】 任子晖；

【作者基本信息】 中国矿业大学 ， 控制理论与控制工程， 2009， 博士

【摘要】 煤矿瓦斯灾害已成为我国煤矿安全生产的最主要威胁和制约煤炭行业可持续发展的最重要因素。认识瓦斯灾害发生的规律和特征、实现瓦斯危险性的准确预测和预警是防治瓦斯灾害的有效手段,也一直是矿山安全及智能信息处理领域的重要研究课题。为此,本文针对目前煤矿安全生产的实际需要,以矿井瓦斯数据为研究对象,以矿井瓦斯预警为目的,系统地进行了基于支持向量机的瓦斯数据融合方法及其应用研究,其主要研究内容如下:1.在矿井瓦斯预警相关概念定义、矿井瓦斯数据流分析、数据融合与支持向量机理论与方法研究的基础上,构建了基于支持向量机的矿井瓦斯数据融合技术框架。2.在粒子群优化和遗传算法研究的基础上,提出了一种基于混沌粒子群优化-遗传算法(CPSO-GA)的支持向量机参数向量的选择与优化方法,为基于支持向量机数据融合的矿井瓦斯预警技术研究创造了技术条件。3.把支持向量机和相空间重构、粗糙集、聚类、非线性组合预测等多种现代信息处理手段进行有效集成,对多源的矿井瓦斯数据在数据级、特征级和决策级等三个层次上进行了以矿井瓦斯预警为目的的数据融合技术研究:(1)在数据级融合层次上,提出了基于支持向量机瓦斯数据的噪声消除方法,有效地消除了一维和高维矿井瓦斯数据中的普通噪声、异常数据和缺失数据的影响,为获取准确和完备的原始矿井瓦斯数据提供了技术手段。(2)在特征级融合层次上,提出了基于相空间重构-聚类-多支持向量机回归的瓦斯混沌时间序列预测方法,以及基于粗糙集-支持向量机的煤与瓦斯突出预测方法,并形成了一个完整的“突出类型→突出强度→突出煤量”的煤与瓦斯突出预测体系,有效地提取了瓦斯数据特征,预测其变化规律和发展趋势。(3)在决策级融合层次上,提出了基于支持向量机的矿井瓦斯涌出量非线性组合预测方法,以及基于多最小二乘支持向量机的CH4、CO、温度和风速等同类/异类传感器融合的多变量决策预测方法,实现了矿井采区安全状态等级的实时评价和安全状态的预警。理论分析和实验结果分析表明,本文提出的支持向量机数据融合技术可有效地应用于矿井瓦斯预警中。更多还原

【Abstract】 Coal mine gas disasters has become a main threat to safe production and the most important factor to constrain sustainable development of the coal industry in China. Understanding thoroughly the law and feature of the coal mine gas disaster, and realizing an accurate forecast and early warning of mine gas hazard are effective means of prevention and treatment of mine gas disaster, and also an important research fields of coal mine safety and intelligent information processing. To meet the present actual needs of the safe production in coal mine, this dissertation makes systematic study on the mine gas early warning technology based on support vector machine and data fusion. The main research contents are as follows:1. On the basis of the definition of related concept of gas warning, analysis of mine gas data flow, study of data fusion and support vector machine(SVM), the technology framework of coal mine gas data fusion based on support vector machine is constructed.2. By intensive study on the principle of particle swarm optimization(PSO) genetic algorithm(GA), the method for selection and optimization of parameter vectors of support vector machine based on chaotic particle swarm optimization-genetic algorithm (CPSO-GA) is proposed, which has created technical conditions for the research of gas early-warning technology based on data fusion combined with support vector machine.3. By using support vector machine, chaos theory, rough sets, clustering, non-linear combination forecast and other modern means of information processing, detailed data fusion technology of multi-source coal mine gas data in data-fusion level, feature-fusion and decision-fusion level are studied:(1) In the data-fusion level, the de-noiseing method for coal mine gas data based on support vector machine is proposed, which effectively eliminates the effects of ordinary noise data, ab-normal data and missing data existing in coal mine gas data, and provides an effective technical means to obtain more accurate and complete original coal mine gas data.(2) In the feature-fusion level, the forecasting methods of gas chaotic time series based on phase space reconstruction-clustering-multi support vector machine regression is proposed and the qualitatively and quantitatively forecasting methods of the types,intensity and coal amount of coal-and-gas outburst based on rough sets and support vector machine is put forward, which effectively help to extract the features information of mine gas data, and to forecast changes laws and development trends of mine gas.(3) In the decision-fusion level, the non-linear combination forecasting methods of gas emission amount is proposed, which reduces the risk of decision-making and improves the forecasting accuracy. The multi-variables decision forecasting method based on least squares support vector machine(LS-SVM) and same/different kinds sensors data fusion of CH4, CO, temperature and wind rate is also brought forward, by which a real-time evaluation model and forecasting model of coal mine safety states are constructed, effectively acquiring the feature vector of coal mine safety states, and realizing early-warning of gas disaster.The theoretical analysis and experimental results demonstrate that the methods proposed in this dissertation are effective and feasible, and can be applied to early-warning of gas disasters in coal mine.更多还原