新疆工程学院安全科学与工程学院;新疆工程学院新疆煤矿灾害智能防控与应急重点实验室;辽宁工程技术大学安全科学与工程学院;辽宁工程技术大学电气与控制工程学院;辽宁工程技术大学工商管理学院;
为有效预防矿山重大事故的发生，降低矿山事故风险，以故障树分析法(Fault Tree Analysis, FTA)为理论基础，揭示矿山重大风险节点的耦合机制，然后建立通用的矿山重大风险评估动态贝叶斯网络(Dynamic Bayesian Network, DBN)。从人、机、环、管四方面筛选影响因素，构建故障树模型，涵盖45个基本事件。通过专家评价语言模糊转化及改进的相似聚合法确定DBN模型参数，以7级语言量表收集6位不同权重专家意见，基于经处理得到的各基本事件先验概率，构建DBN模型进行正向推理。将时间划分为九个时间片，在无证据输入下，发现部分节点风险概率随时间上升，矿山风险总体呈上升趋势。反向诊断假设矿山典型重大风险预测风险状态概率100%,计算节点后验概率及变异风险(Risk of Variability, ROV)值并排序，确定人员技术水平差、文化水平低等为主要因素，产品储存量过多、车辆违规操作等为关键因素。最后以某矿山为例开展分析验证工作。研究表明：所构建模型能够基于输入证据准确预测出矿山重大风险概率的变化；通过分析新疆某矿山，成功对关键风险因素进行识别，并对这些风险因素进行排序，从而识别出系统的薄弱环节，并实现风险监控，决策者因此可以迅速做出反应，减少事故风险。
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基本信息:
DOI：10.13637/j.issn.1009-6094.2024.1616
中图分类号:TD77;TP18
引用信息:
[1]陆卫东,孙胤洲,杨婷等.改进FTA-DBN在矿山典型重大风险预测中的应用[J].安全与环境学报,2025,25(04):1286-1297.DOI:10.13637/j.issn.1009-6094.2024.1616.
基金信息:
新疆维吾尔自治区重点研发计划项目(2022B03031-1)