Abstract
The manned submersible is the main platform for precise deep-sea exploration. The cognitive states of different levels generated when the submerged members in the vehicle are faced with changes in external factors, such as environmental characteristics and operational tasks at different operation stages, will have a dynamic impact on work efficiency and human reliability. Based on the CREAM method and Bayesian network, this study proposed a human reliability assessment method and risk prediction technology for deep submergence operating system. Firstly, the extended CREAM method was corrected again by introducing the cognitive performance correction coefficient. Secondly, the weights of common performance conditions (CPCs) and cognitive effectiveness influence system (CEIS) were adaptively calibrated by using the DEMATEL method. Finally, the Bayesian assessment network for human reliability of deep submergence operating system was constructed to assess the human reliability of each operation stage and the overall system. This method is helpful for the overall design of the manned deep submergence research institute, and it can accurately identify and interdict the key risk points for relevant technicians of system security engineering, so as to reduce the safety risk of deep submergence operating system and improve the working efficiency of the man-machine system in cabin.
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