基于CM−HFACS模型和GA−ANN优化算法的煤矿轻微伤事故致因分析

Analysis of the causes of minor injury accidents in coal mines based on improved HFACS model and GA−ANN optimized algorithm

  • 摘要: 为深入分析以人为主因的煤矿轻微伤事故致因表现形式及其相互间耦合关系,以淮南矿业集团2022—2023年期间87起轻微伤事故为研究对象,从煤矿组织影响、不安全监管、不安全行为前提、不安全行为、事故应急处置5个层级改进CM−HFACS(Human Factor Analysis and Classification System of Coal Mine,煤矿人因分析与分类系统)事故致因模型,并采用GA−ANN(Genetic Algorithm−Artificial Neural Network,遗传算法−人工神经网络)优化算法对致因因素进行建模,开展节点度、中心性、聚类系数及重要性分析。结果表明,(1)高连接度节点中的稠密子网为事故关键致因表现形式;(2)安全资金投入不足、人员分配不合理、防护器具不规范为事故蔓延的“交通要塞”;(3)安全资金投入不足、跟班监督检查不到位、人员分配不合理为事故链源头或链中位置;(4)自行处置不合理、延误申报伤情、安全奖惩不到位、只凭经验判断风险、未制订落实相关管控措施、做事急躁、未按程序上报事故、对设备误操作为暴露风险和局部传播潜力的“火药桶”;(5)未察觉危险靠近、对岗位操作不熟悉、风险识别后未及时通报、未辨识出相关作业风险揭示了事故关键失控因素;(6)技能不符合岗位要求、自行处置不合理、违章指挥为引发事故的“高危群体”;(7)延误申报伤情、疲劳作业、未关停设备就进行检修、班前会召开质量低为综合排名居前的关键致因表现形式。该研究可为煤矿轻微伤事故精准防控提供支撑。

     

    Abstract: To deeply analyze the manifestation forms and mutual coupling relationships of human−caused minor injury accidents in coal mines, this study takes 87 minor injury accidents that occurred in Huainan Mining Group from 2022 to 2023 as the research object. The CM−HFACS (Human Factor Analysis and Classification System of Coal Mine) accident causation model is improved from five levels: coal mine organizational influence, unsafe supervision, unsafe behavior premise, unsafe behavior, and accident emergency response. The GA−ANN (Genetic Algorithm−Artificial Neural Network) optimization algorithm is used to mathematically model the causation factors, and node degree, centrality, clustering coefficient, and importance analysis are conducted. The results show that: (1) Dense subnets in the high connection degree nodes are the key manifestation form of the accident cause. (2) Insufficient safety fund investment, unreasonable personnel allocation, and non−standard protective equipment are the "traffic bottlenecks" for the spread of the accident. (3) Insufficient safety fund investment, inadequate on−shift supervision and inspection, and unreasonable personnel allocation are the sources or positions in the middle of the accident chain. (4) Unreasonable self−disposal, delayed injury reporting, inadequate safety rewards and punishments, risk judgment based solely on experience, failure to formulate and implement relevant control measures, impatience, failure to report accidents according to procedures, and misoperation of equipment are the "powder kegs" that expose risks and have local transmission potential. (5) Failure to notice approaching danger, unfamiliarity with job operations, failure to report risks in a timely manner after identification, and failure to identify relevant job risks reveal the key factors of loss of control in the accident. (6) Inadequate skills for the job, unreasonable self−disposal, and violation of command are the "high−risk groups" that trigger accidents. (7) delayed injury reporting, fatigue work, maintenance without shutting down equipment, and low−quality pre−shift meetings are the key manifestation forms of causation with the highest comprehensive ranking. This research can provide support for the precise prevention and control of minor injury accidents in coal mines.

     

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