PDF(Pubmed)
Abstract：
Failures in mining machinery can abruptly halt mineral production and operations, emphasizing the indispensable role of humans in maintenance and repair operations. Addressing human errors is crucial for ensuring a safe and reliable system, particularly during maintenance activities where accidents frequently occur. This paper focuses on evaluating Human Reliability (HR) to enhance activity implementation effectiveness. Given the challenge of limited and uncertain data on human errors, this study aims to estimate the probability of human errors using Bayesian networks (BN) under uncertain parameters. Applying this approach to assess HR in the maintenance and repair operations of mining trucks at Golgohar Iron Ore Mine in Iran, the study identifies critical factors influencing error occurrence in a fuzzy environment. The results highlight key factors impacting human error and offer insights into estimating HR with minimal human intervention.
摘要：
矿山机械的故障会突然停止矿物生产和运营，强调人类在维护和维修操作中不可或缺的作用。解决人为错误对于确保系统安全可靠至关重要，特别是在事故频繁发生的维护活动中。本文着重于评估人的可靠性（HR），以提高活动执行的有效性。考虑到关于人为错误的有限和不确定数据的挑战，本研究旨在在不确定参数下使用贝叶斯网络（BN）估计人为错误的概率。应用这种方法来评估伊朗Golgohar铁矿矿用卡车的维护和维修操作中的HR，该研究确定了模糊环境中影响错误发生的关键因素。结果突出了影响人为错误的关键因素，并提供了以最少的人为干预估计HR的见解。
