摘要
为实现工作面瓦斯异常涌出的动态、实时预警,对工作面瓦斯浓度时间序列的概率分布进行分析,利用Shapiro-Wilk和Lilliefors联合正态检验的方法,深入挖掘工作面瓦斯浓度时间序列的分布特征;以潘三矿某掘进工作面为例,实时正态检验工作面过断层时的瓦斯浓度时间序列。结果表明：当影响工作面瓦斯涌出因素作用比较均匀、单一因素不起决定性作用时,瓦斯浓度时间序列服从正态分布;当不服从正态分布时,则断层对工作面瓦斯涌出影响显著,有可能发生灾害。通过对工作面瓦斯浓度时间序列进行实时正态检验以辨识瓦斯涌出状态,将瓦斯浓度时间序列的分布特征作为预警的依据,能为瓦斯灾害的预测预警起有效的辅助作用。
For the purpose of the dynamic and real-time early warning of abnormal gas emission,the probability distribution of the time series of gas concentration at the working face was analyzed. The joint normal test of Shapiro-Wilk and Lilliefors was used to deeply excavate the distribution characteristics of time series of gas concentration at the working face. Taking a driving face in Pansan coal mine as an example,a real-time normal test of the time series of gas concentration during fault crossing was carried out. The research results show that when the factors influencing gas emission are similar in effect and none of them plays a decisive role,the time series of gas concentration is normally distributed,that when disobeying the normal distribution,the fault has a significant influence on the gas emission at the working face,which may lead to disasters,that through the real-time normal test of the time series of gas concentration in the working face,the gas emission state can be identified,and that the distribution characteristics of the time series of gas concentration can be taken as the basis of the early warning,which can play a helpful role in the prediction and early warning of gas disasters.
作者
杨艳国
穆永亮
秦洪岩
YANG Yanguo;MU Yongliang;QIN Hongyan(School of Mining, Liaoning Technical University, Fuxin Liaoning 123000, China;Major Scientific and Technological Platform of Universities in Liaoning- Research Center of Coal Resources Safe mining and Clean Utilization Engineering, Fuxin Liaoning 123000, China;School of Safety Engineering, North China Institute of Science and Technology, Beijing 101601, China)
出处
《中国安全科学学报》
CAS
CSCD
北大核心
2018年第3期120-125,共6页
China Safety Science Journal
关键词
瓦斯浓度
时间序列
正态分布
假设检验
异常辨识
灾害预警
gas concentration
time series
normal distribution
hypothesis test
identification of abnormality
disaster warning
