A simplified bi-variable human error probability calculation method is developed by incorporating two common performance condition( CPC) factors, which are modified from factors employed in cognitive reliability and e...A simplified bi-variable human error probability calculation method is developed by incorporating two common performance condition( CPC) factors, which are modified from factors employed in cognitive reliability and error analysis method(CREAM) to take into account the characteristics of shipping operations. After the influencing factors are identified, Markov method is used to calculate the values of human reliability. The proposed method does not rely on the involvement of experts in the field of human factor nor depend on historical accidents or human error statistics. It is applied to the case of the crew on board of an ocean going dry bulk carrier. The caculated results agree with the actual case, which verifies the validity of the model.展开更多
基金Supported by the National Basic Research Program of China("973"Program,No.2014CB046804)National Natural Science Foundation of China(No.51239008)+1 种基金Foundation of State Key Laboratory of Marine Engineering of Shanghai Jiaotong UniversityFoundation for Innovative Research Groups of National Natural Science Foundation of China(No.51021004)
文摘A simplified bi-variable human error probability calculation method is developed by incorporating two common performance condition( CPC) factors, which are modified from factors employed in cognitive reliability and error analysis method(CREAM) to take into account the characteristics of shipping operations. After the influencing factors are identified, Markov method is used to calculate the values of human reliability. The proposed method does not rely on the involvement of experts in the field of human factor nor depend on historical accidents or human error statistics. It is applied to the case of the crew on board of an ocean going dry bulk carrier. The caculated results agree with the actual case, which verifies the validity of the model.
