在采矿卡车的维护和维修操作中对人类可靠性的分析:贝叶斯网络方法
Ali Reza Zaker Hossein1, Ahmad Reza Sayadi1, Mohammad Javad Rahimdel2
1Department of Mining Engineering, Faculty of Engineering, Tarbiat Modares University, Iran.
Heliyon
|August 15, 2024
概括
在采矿机械维护中,人为错误至关重要. 本研究使用贝叶斯网络来估计人类可靠性 (HR) 并确定关键错误因素,以有限的数据提高安全性和效率.
科学领域:
- 工业工程 工业工程 工业工程
- 采矿工程 采矿工程 采矿工程
- 风险管理 风险管理
背景情况:
- 矿山机械的故障会破坏生产,强调需要有效的人工维护.
- 维护过程中的人为错误是事故和系统不可靠性的重要原因.
- 准确的人员可靠性 (HR) 评估对于采矿业的运营安全和效率至关重要.
研究的目的:
- 在采矿卡车维护和维修操作中评估人力可靠性 (HR).
- 解决工业环境中关于人为错误的有限和不确定的数据的挑战.
- 在模糊的环境中识别影响人类错误发生的关键因素.
主要方法:
- 利用贝叶斯网络 (BN) 在不确定性下对人类错误进行概率估计.
- 应用了BN方法来评估伊朗戈尔戈哈尔铁矿石矿的矿用卡车维护和维修的人力资源.
- 整合了模糊环境考虑,以处理关于人为错误的不准确数据.
主要成果:
- 确定了在采矿机械维护过程中显著影响人为错误概率的关键因素.
- 使用BN成功估计了HR,即使对人为错误的数据有限且不确定.
- 该研究为在复杂的工业维护场景中评估人力资源提供了一个框架.
结论:
- 人类错误是采矿机械维护的关键因素;有效的人力资源评估是必不可少的.
- 贝叶斯网络提供了一种可靠的方法来估计HR,并在不确定的环境中识别影响错误的因素.
- 这些发现为尽量减少人类干预和提高采矿操作安全提供了实际见解.
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