Research on self-renewing leakage detection technology of coal mine gas drainage pipe network system
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摘要: 煤矿井下工作环境恶劣,瓦斯抽采管道易受到碰撞、落煤坠砸等损伤而造成漏气,当巷道内空气大量进入管网系统后,管网内瓦斯抽采浓度可能远低于钻孔孔口浓度。针对该问题,提出了一种基于多元高斯声束模型的煤矿瓦斯抽采管网系统自更新检漏技术。采用多元高斯声束模型对漏气点声音进行加强处理,并分析煤矿井下抽采管网系统的主要漏气类型和噪声来源,建立漏气模型和噪声模型;将采集的声音样本与预存模型进行比对,判断是否存在漏气现象,并将使用环境中出现频率超过30%的声音样本自动存储为漏气模型,实现模型的自动更新,提高检漏准确性;基于自更新检漏技术研发了YJL40检漏仪,其主要部件包括探测头、金属软管、主机和报警器。利用自更新检漏技术及相应产品对高家庄煤矿的高、低负压抽采系统共计7 585 m管道进行检漏,将检测出的漏气点进行有效封孔后,抽采终端瓦斯体积分数分别提高了37.1%和28%,验证了自更新检漏技术的有效性。Abstract: Working environment in underground coal mine is harsh, and gas drainage pipelines are vulnerable to collision, coal falling and other injuries, resulting in gas leakage. When a large amount of air in tunnel enters pipe network system, the concentration of gas drainage in pipe network may be much lower than the concentration at the drilling hole. For the above problems, a self-renewing leakage detection technology of coal mine gas drainage pipe network system based on multi-Gaussian beam model was proposed. Multi-Gaussian beam model is adopted to strengthen the processing of the sound of the leakage points, main leakage types and noise sources of underground gas drainage pipe network system are analyzed, and leakage model and noise model are established.The collected sound samples are compared with pre-stored models to determine whether there is gas leakage, and the sound samples that occur more than 30% in the use environmen are automatically stored as gas leakage models to realize automatic model update and improve leakage detection accuracy. YJL40 leakage detector is developed based on self-renewing leakage detection technology, its main components include probe, metal hose, host and alarm. Self-renewing leakage detection technology and corresponding products are applied in high and low negative pressure drainage system in Gaojiazhuang Coal Mine for leakage detection of totals 7 585 m of pipelines, after the detected leakage points are effectively sealed, the gas volume fraction in drainage terminal is increased by 37.1% and 28% respectively, verifying the effectiveness of the self-renewing leakage detection technology.
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