基于FA-Logistic的煤矿瓦斯突出事故安全预警研究
发布时间：2019-02-22 09:52
【摘要】：我国煤矿瓦斯突出事故时有发生,造成的损失巨大。基于此,对新常态下的瓦斯突出事故进行预警具有重要意义。通过研究近年来瓦斯突出事故发生状况,选取煤层瓦斯含量、埋藏深度、厚度等十个关键影响因素作为评价指标体系,以获取的18组数据作为研究样本,借助SPSS软件,运用因子分析——Logistic回归方法进行综合预警,另外18组数据作为检验样本,结果都表明该方法有很好的预警效果,总体预警准确率达到80%以上。因此,该综合预警方法可以较早预测煤矿瓦斯突出事故的发生。
[Abstract]:Coal mine gas outburst accidents occur from time to time in China, resulting in huge losses. Based on this, it is of great significance to warn the gas outburst accident under the new normal condition. By studying the occurrence of gas outburst accidents in recent years, ten key influencing factors, such as coal seam gas content, burial depth, thickness and so on, are selected as the evaluation index system. The 18 groups of data obtained are taken as research samples and SPSS software is used. The factor analysis-Logistic regression method is used for comprehensive early warning, and the other 18 groups of data are used as test samples. The results show that the method has a good early warning effect, and the overall accuracy of early warning is over 80%. Therefore, the comprehensive early-warning method can predict the occurrence of coal mine gas outburst earlier.
【作者单位】： 安徽理工大学经济与管理学院;
【基金】：安徽省人文社科重点基地研究基金(SK2016A0279、SK2015A081)
【分类号】：TD713
[Abstract]:Coal mine gas outburst accidents occur from time to time in China, resulting in huge losses. Based on this, it is of great significance to warn the gas outburst accident under the new normal condition. By studying the occurrence of gas outburst accidents in recent years, ten key influencing factors, such as coal seam gas content, burial depth, thickness and so on, are selected as the evaluation index system. The 18 groups of data obtained are taken as research samples and SPSS software is used. The factor analysis-Logistic regression method is used for comprehensive early warning, and the other 18 groups of data are used as test samples. The results show that the method has a good early warning effect, and the overall accuracy of early warning is over 80%. Therefore, the comprehensive early-warning method can predict the occurrence of coal mine gas outburst earlier.
【作者单位】： 安徽理工大学经济与管理学院;
【基金】：安徽省人文社科重点基地研究基金(SK2016A0279、SK2015A081)
【分类号】：TD713
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