中国安全科学学报 ›› 2022, Vol. 32 ›› Issue (8): 146-153.doi: 10.16265/j.cnki.issn1003-3033.2022.08.1633

• 安全工程技术 • 上一篇    下一篇

DS理论-贝叶斯网络下的煤矿通风系统风险评估*

李金蓉(), 杨玉中**()   

  1. 河南理工大学 能源科学与工程学院,河南 焦作 451460
  • 收稿日期:2022-01-09 修回日期:2022-05-10 出版日期:2022-09-05 发布日期:2023-02-28
  • 通讯作者: 杨玉中
  • 作者简介:

    李金蓉 (1998—),女,宁夏中卫人,硕士研究生,主要研究方向为煤矿安全风险评价。E-mail:

    杨玉中,教授。

  • 基金资助:
    NSFC-河南联合基金重点项目(U1904210); 国家自然科学基金资助(51874121); 河南省高校基本科研业务费专项资金资助(NSFRF180104); 河南省重点科技攻关计划项目(182102310002)

Risk assessment of ventilation system in coal mines based on DS theory and Bayesian network

LI Jinrong(), YANG Yuzhong**()   

  1. School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo Henan 451460, China
  • Received:2022-01-09 Revised:2022-05-10 Online:2022-09-05 Published:2023-02-28
  • Contact: YANG Yuzhong

摘要:

为了评估煤矿通风系统风险等级并确定敏感指标,首先基于通风系统的特点及相关文献标准,从"人、机、环、管"4个角度,选取16个主要指标,并建立评价指标体系;然后引入可有效融合信息的DS理论-贝叶斯网络模型,以构建煤矿通风系统风险评估模型;最后以河南某煤矿的实际调研数据为例,计算指标权重和风险概率,进行风险推理和敏感性分析,得到该矿通风系统风险等级和敏感指标。研究结果表明:该煤矿通风系统的风险等级为一般,敏感指标为通风巷道失修率、回风段阻力占比、人员平均工龄和主要通风机效率。

关键词: 煤矿通风系统, DS理论, 贝叶斯网络, 风险评估, 指标体系, 敏感性分析

Abstract:

In order to evaluate risk level of ventilation system in coal mines, firstly, an assessment index system was established with 16 main indicators from perspectives of human, equipment, environment and management according to characteristics of ventilation system, and related literatures and technical standards. Then, DS theory-Bayesian network model that could achieve information fusion was introduced to establish a risk assessment model. Finally, with actual investigation data from a coal mine in Henan province as an example, index weight and risk probability were calculated, and its risk level and sensitive indicators were obtained through risk reasoning and sensitivity analysis. The results show that the ventilation system is at a general risk level, and its sensitivity indicators include disrepair rate of ventilation laneway, percentage of return air resistance, average length of service and efficiency of main ventilation fan.

Key words: ventilation system in coal mine, DS theory, Bayesian network, risk assessment, indicator system, sensitivity analysis