A Bayesian method for estimating human error probability（HEP） is presented.The main idea of the method is incorporating human performance data into the HEP estimation process.By integrating human performance data an...A Bayesian method for estimating human error probability（HEP） is presented.The main idea of the method is incorporating human performance data into the HEP estimation process.By integrating human performance data and prior information about human performance together,a more accurate and specific HEP estimation can be achieved.For the time-unrelated task without rigorous time restriction,the HEP estimated by the common-used human reliability analysis（HRA） methods or expert judgments is collected as the source of prior information.And for the time-related task with rigorous time restriction,the human error is expressed as non-response making.Therefore,HEP is the time curve of non-response probability（NRP）.The prior information is collected from system safety and reliability specifications or by expert judgments.The（joint） posterior distribution of HEP or NRP-related parameter（s） is constructed after prior information has been collected.Based on the posterior distribution,the point or interval estimation of HEP/NRP is obtained.Two illustrative examples are introduced to demonstrate the practicality of the aforementioned approach.展开更多
The accurate quantification of human error probability(HEP) has long been a main pursuit for most human reliability analysis(HRA) methods. This paper proposes a strategy to analyzeand predict human error on the basis ...The accurate quantification of human error probability(HEP) has long been a main pursuit for most human reliability analysis(HRA) methods. This paper proposes a strategy to analyzeand predict human error on the basis of a further modification of theexisting cognitive reliability and error analysis method (CREAM)in HRA. Through providing a broader definition for the key parametersused in the quantification method, this paper produces arelatively flexible strategy to determine the nominal HEP (HEP0).Basing on this strategy, the actual quantification of HEP in CREAMis able to be applied to more verified conditions. To prove the validityof the method proposed, a case study of spacecraft launch isalso introduced, in which the prediction derived from the method isproved consistent with real field data.展开更多
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(20114307120032)the National Natural Science Foundation of China(71201167)
文摘A Bayesian method for estimating human error probability（HEP） is presented.The main idea of the method is incorporating human performance data into the HEP estimation process.By integrating human performance data and prior information about human performance together,a more accurate and specific HEP estimation can be achieved.For the time-unrelated task without rigorous time restriction,the HEP estimated by the common-used human reliability analysis（HRA） methods or expert judgments is collected as the source of prior information.And for the time-related task with rigorous time restriction,the human error is expressed as non-response making.Therefore,HEP is the time curve of non-response probability（NRP）.The prior information is collected from system safety and reliability specifications or by expert judgments.The（joint） posterior distribution of HEP or NRP-related parameter（s） is constructed after prior information has been collected.Based on the posterior distribution,the point or interval estimation of HEP/NRP is obtained.Two illustrative examples are introduced to demonstrate the practicality of the aforementioned approach.
基金supported by the National Natural Science Foundation of China(7117100871571004)
文摘The accurate quantification of human error probability(HEP) has long been a main pursuit for most human reliability analysis(HRA) methods. This paper proposes a strategy to analyzeand predict human error on the basis of a further modification of theexisting cognitive reliability and error analysis method (CREAM)in HRA. Through providing a broader definition for the key parametersused in the quantification method, this paper produces arelatively flexible strategy to determine the nominal HEP (HEP0).Basing on this strategy, the actual quantification of HEP in CREAMis able to be applied to more verified conditions. To prove the validityof the method proposed, a case study of spacecraft launch isalso introduced, in which the prediction derived from the method isproved consistent with real field data.
