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Root Cause Analysis of Human Errors in Complex System Accdidehts

复杂系统事故中人为错误的根本原因分析

基本信息

批准号：
12680441
负责人：
INOUE Koichi
金额：
$ 2.05万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680441/
关键词：
Complex System Accidents Human Errors Root Cause Mental Images Error Classification Rule Component Failure Situation Awareness Action Generation

项目摘要

The object of this research is to establish a method to identify root causes of a system accident using the knowledge on human errors obtained in human factors, and cognitive engineering etc.At the first stage in Heisei 12, a framework was considered to identify root causes of human errors and component failure. To estimate the human cognitive process, a concept of 'mental images (MI)' was applied. From the viewpoint of how to recognize situations (generation of MI), how to make a decision on what to do (action planning based on MI), and how to act (execution based on MI), errors are identified as deviations from the desired criterion. Application to marine accidents showed that root causes were obtained and the merits of the method were confirmed. Further, as the situation awareness was essential, how to allocate monitoring points was derived to identify the system state. Related to system state identification, how to evaluate the effect of a potential component failure on the system function was also considered.In the second stage in Heisei 13, the analysis method to identify root causes of human errors was improved, and further problems to be solved were clarified. Based on MI, the surface human errors were classified into errors in acquisition of input information, errors in judgment, and errors in action execution, Based on MI formation from input imformation sources, the framework was improved to identify root causes of each type of errors. By applying the proposed method to a ship accident, its merits were confirmed. Further, a derivation method of accident sequences was proposed to consider safety measures to prevent accidents in a complex system. The method is based on a global system model composed of hardware behavior such as components, software behavior such as computerized actions, and human actions.

本研究的目的是建立一种方法，利用从人为因素中获得的关于人为错误的知识，以及认知工程等知识来识别系统事故的根本原因。在平成12的第一阶段，考虑了一个框架来识别人为错误和部件故障的根本原因。为了估计人类的认知过程，我们使用了“心理意象”的概念。从如何识别情况(MI的产生)、如何决定做什么(基于MI的行动计划)和如何行动(基于MI的执行)的角度来看，错误被识别为偏离期望的标准。在海上事故中的应用表明，得到了根本原因，并证实了该方法的优点。此外，由于态势感知是必不可少的，因此推导了如何分配监测点来识别系统状态。与系统状态识别相关，还考虑了如何评估潜在部件故障对系统功能的影响。在平成13的第二阶段，改进了识别人为错误根本原因的分析方法，并阐明了进一步需要解决的问题。基于MI，将表层人为错误分为输入信息获取错误、判断错误和行动执行错误，并根据输入信息源的MI形成改进框架，以确定每类错误的根本原因。通过在一起船舶事故中的应用，验证了该方法的优越性。在此基础上，提出了一种事故序列的推导方法，以考虑复杂系统中预防事故的安全措施。该方法基于全局系统模型，该全局系统模型由诸如组件的硬件行为、诸如计算机化动作的软件行为和人类动作组成。