基于CREAM方法的人因失效概率预测模型研究  

Study on prediction model of human factor failure probability based on CREAM

作　　者：廖斌[1] 杨琴[1] 鲁茂[1] 罗瑶[1] 

机构地区：[1]四川师范大学商学院,成都610101

出　　处：《中国安全生产科学技术》2012年第7期46-50,共5页Journal of Safety Science and Technology

基　　金：四川省教育厅科研项目(编号:12SB087);四川省哲学社会科学"十二五"规划项目(编号:SC11C042)

摘　　要：认知可靠性与差错分析方法(CREAM)是第二代人因可靠性分析方法中的代表方法之一,通过对任务环境进行分析从而直接确定人为差错发生概率。本文分析了该方法及其后续研究在人因可靠度评估时存在的主要问题,并以CREAM方法为基础建立新的人因失效概率预测模型。模型首先要求有针对性的对具体任务环境确定通用性能影响因子(CPC)权重,然后通过对CPC进行打分对任务环境进行量化,通过加权求和的方式分别计算出CPC的改进总分值G和降低总分值J,最后运用新建的预测模型计算出人因失效概率。新模型提出了三点改进:第一将任务环境设定为连续的空间;第二提出了不同的工作环境(任务环境)应该有其对应的CPC因子权重;第三考虑正影响CPC因子和负影响CPC因子的双重影响,建立双变量预测模型,预测结果更加合理。Cognitive reliability and error analysis method （ CREAM ） is one of the representative second-generation human reliability analysis methods, which is used to determine the probability of human errors by analyzing the task context. In this paper some problems of human reliability probability assessment in CREAM and its follow-up study were presented. Aiming at these problems, a new prediction model of human failure probability was set up. This model required to put forward the weights of common performance condition （CPCs） according to the task context firstly. Then, the task context was quantified by grading the common performance conditions （CPCs） , sum up all the improved CPCs scores into G and sum up all the reduced CPCs scores into J. At last the human failure probabil- ity could be calculated by the prediction model. The model put forward three improvement： Firstly the task context should be continuous space. Secondly the weights of common performance condition（CPCs） should be set accord- ing to the specific working environment. Thirdly the model is a dual variable prediction model based on considering the positive effect of CPCs and the negative effect of CPCs, the prediction result will be more reasonable.

关 键 词：人因可靠性分析(HRA) 认知可靠性与差错分析方法(CREAM) 通用性能因子(CPC) 失效概率 预测模型 

分 类 号：X913.4[环境科学与工程—安全科学]