付净, 傅贵, 聂方超, 刘虹, 王泽恒. 煤矿事故不安全动作原因识别及作用研究[J]. 煤矿安全, 2020, 51(1): 242-245.
    引用本文: 付净, 傅贵, 聂方超, 刘虹, 王泽恒. 煤矿事故不安全动作原因识别及作用研究[J]. 煤矿安全, 2020, 51(1): 242-245.
    FU Jing, FU Gui, NIE Fangchao, LIU Hong, WANG Zeheng. Causes Identification and Influence of Unsafe Acts in Coal Mine Accidents[J]. Safety in Coal Mines, 2020, 51(1): 242-245.
    Citation: FU Jing, FU Gui, NIE Fangchao, LIU Hong, WANG Zeheng. Causes Identification and Influence of Unsafe Acts in Coal Mine Accidents[J]. Safety in Coal Mines, 2020, 51(1): 242-245.

    煤矿事故不安全动作原因识别及作用研究

    Causes Identification and Influence of Unsafe Acts in Coal Mine Accidents

    • 摘要: 为了有效减少由不安全动作引发的煤矿事故,基于FTA和24 Model,构建不安全动作识别及作用分析模型;通过实证研究,具体化6步骤应用过程,共识别出不安全动作致因因素22项,涉及30条作用路径。结果表明:FTA识别程序可有效展示各致因要素间逻辑关系,UA-UA及UA-UC共涉及20条路径,关联性较强;Gephi0.9.2分类特征可视化突出显示动作分类、人员类别及具体违章条目之间的关联性,违章操作占违章动作总数54.6%,共违反法规条款25项。

       

      Abstract: To effectively reduce coal mine accidents caused by unsafe acts, it is necessary to construct the identification and role analysis model based on FTA and 24 Model. Through empirical research, the 6-step application process was concretized and 22 causes of unsafe actions were identified, involving 30 action paths. The results show that the FTA identification program can effectively display the logical relationship among various factors, and UA-UA and UA-UC involve a total of 20 paths, with a strong correlation. Gephi0.9.2 classification features visualization highlights the correlation between the classification of actions, categories of personnel and specific violations, with violations accounting for 54.6% of the total number of violations, a total of 25 articles of the regulations.

       

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