使用基于IT2F和Z数的贝叶斯网络进行过程事故预测:球形坦克的案例研究
Mostafa Mirzaei Aliabadi1, Rouzbeh Abbassi2, Omid Kalatpour1
1Center of Excellence for Occupational Health, Occupational Health and Safety Research Center, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
PloS one
|August 29, 2024
概括
本研究引入了一种新的贝叶斯网络 (BN) 方法,使用间隔类型-2模糊集 (IT2FS) 和Z数进行动态风险评估,减少事故预防的不确定性. 该方法为行业经理提供了更可靠的理解.
科学领域:
- 风险评估和风险管理
- 模糊逻辑在工程中的模糊逻辑
- 贝叶斯网络用于安全分析.
背景情况:
- 传统的风险评估方法经常与专家判断和数据固有的不确定性作斗争.
- 动态风险评估对于不断变化的工业环境和事故预防至关重要.
- 整合模糊逻辑和贝叶斯网络可以提高处理不准确和不确定的信息.
研究的目的:
- 提出一种新的动态风险评估方法,使用带有模糊数据的贝叶斯网络 (BN).
- 通过整合间隔类型-2模糊集 (IT2FS) 和Z数来降低风险评估中的不确定性.
- 为工业环境中的事故预防绩效提供一个更可靠的框架.
主要方法:
- 使用系统危险识别,预测和预防 (SHIPP) 方法,顶级事件断层树和障碍故障断层树构建一个蝶结图.
- 对先前概率的专家意见的量化,使用IT2FS和Z数来减少不确定性.
- 用贝叶斯更新与β分布和记录数据计算关键基本事件和障碍失败的后期概率.
主要成果:
- 该方法成功计算了5年内屏障故障的后期概率和后果.
- 与IT2FS单独相比,IT2FS-Z方法显示了较浅的结果概率上升趋势,这是由于专家的信心水平.
- 观察到的结果差异更明显,差异更大 (10-4比10-5),突出了不确定性量化的影响.
结论:
- 拟议的基于BN的模糊动态风险评估方法有效地减少了与传统方法相比的不确定性.
- 通过整合IT2FS和Z号码,通过增加专家信心,提高了风险评估的可靠性.
- 这项研究为行业管理人员提供了一个更全面,更可靠的工具来优化事故预防策略.
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