通过结构分析和系统动态,通过工业维护中人为错误的动态建模
Vahideh Bafandegan Emroozi1, Mostafa Kazemi1, Alireza Pooya1
1Department of Management, Faculty of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.
在维护中量化人为错误概率 (HEP) 是防止事故的关键. 本研究使用MICMAC和系统动态来建模影响因素,并预测人类错误,以便更好地做出决策.
科学领域:
- 工业工程 工业工程 工业工程
- 职业安全与健康问题 职业安全与健康问题
- 风险管理 风险管理
背景情况:
- 人类错误是造成工业事故和操作中断的主要原因,特别是在维护过程中.
- 量化人为错误概率 (HEP) 和理解影响因素对于降低风险至关重要.
- 性能塑造因素 (PSF) 动态影响人为错误,需要预测建模.
研究的目的:
- 用PSF来确定和模拟水泥厂维护任务中的HEP.
- 分析影响人类错误,职业事故和成本的因素的相互依赖和影响.
- 预测随着时间的推移的人类错误行为,并支持管理决策.
主要方法:
- 交叉影响矩阵乘法应用于分类 (MICMAC) 分析,以评估因素关系和影响.
- 系统动力学 (SD) 建模用于预测系统行为和模拟各种场景.
- 将MICMAC分析与SD集成在一起,以优化人类错误预测.
主要成果:
- 在MICMAC分析中,确定和分类了因素对HEP,事故和成本的依赖性和影响.
- 集成的MICMAC-SD模型为预测人类错误动态提供了一个框架.
- 创建了多个场景来说明基于HEP变化的潜在结果.
结论:
- 了解和量化HEP,考虑到动态PSF,对于有效的维护风险管理至关重要.
- MICMAC-SD方法提供了一种可靠的方法来预测和管理人类错误.
- 基于HEP预测的知情管理决策可以提高安全性并减少运营中断.
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