Fuzzy Logic in HEART and CREAM Methods to Assess Human Error and Find an Optimum Method Using a Hierarchical Fuzzy System: A Case Study in a Steel Factory

Rezie Boroun, Yaser TAHMASBI BIRGANI, Z. Mosavianasl, Gholamabas Shirali
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引用次数: 2

Abstract

Numerous studies have been conducted to assess the role of human errors in accidents in different industries. Human reliability analysis (HRA) has drawn a great deal of attention among safety engineers and risk assessment analyzers. Despite all technical advances and the development of processes, damaging and catastrophic accidents still happen in many industries. Human Error Assessment and Reduction Technique (HEART) and Cognitive Reliability and Error Analysis Method (CREAM) methods were compared with the hierarchical fuzzy system in a steel industry to investigate the human error. This study was carried out in a rolling unit of the steel industry, which has four control rooms, three shifts, and a total of 46 technicians and operators. After observing the work process, reviewing the documents, and interviewing each of the operators, the worksheets of each research method were completed. CREAM and HEART methods were defined in the hierarchical fuzzy system and the necessary rules were analyzed. The findings of the study indicated that CREAM was more successful than HEART in showing a better capability to capture task interactions and dependencies as well as logical estimation of the HEP in the plant studied. Given the nature of the tasks in the studied plant and interactions and dependencies among tasks, it seems that CREAM is a better method in comparison with the HEART method to identify errors and calculate the HEP.  
HEART和CREAM方法中的模糊逻辑用于评估人为错误并使用层次模糊系统找到最佳方法——以钢铁厂为例
为了评估人为失误在不同行业事故中的作用,已经进行了大量研究。人的可靠性分析(HRA)已经引起了安全工程师和风险评估分析人员的极大关注。尽管技术进步和工艺发展,但许多行业仍会发生破坏性和灾难性事故。针对某钢铁企业的人因失误问题,将人因失误评估与减少技术(HEART)、认知可靠性与误差分析方法(CREAM)与层次模糊系统进行了比较研究。这项研究是在钢铁行业的一个轧制装置中进行的,该装置有四个控制室,三个班次,共有46名技术人员和操作员。在观察了工作过程、审查了文件并采访了每一位操作员之后,完成了每种研究方法的工作表。在层次模糊系统中定义了CREAM和HEART方法,并分析了必要的规则。研究结果表明,CREAM比HEART更成功地显示出更好的能力来捕捉任务的相互作用和依赖性,以及对所研究植物中HEP的逻辑估计。考虑到所研究工厂中任务的性质以及任务之间的相互作用和依赖性,与HEART方法相比,CREAM似乎是一种更好的方法来识别错误并计算HEP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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