Human reliability analysis for manned submersible diving process based on CREAM and Bayesian network

The diving mission of manned submersibles is a long-term, high-intensity work that is affected by many factors and is in a narrow confined space. In order to improve the reliability of oceanauts' safe operations, this paper is based on the cognitive reliability and error analysis method (CREAM) and the Bayesian network method to study the human errors of the diving mission. First, we construct a Bayesian network framework of the diving process by analyzing the diving steps. Second, the CREAM is applied to calculate the prior probability of each root node's error. Then, the backward reasoning ability of the Bayesian network is used to calculate the posterior probabilities and identify the top few risk nodes. Finally, we obtained the top few risk factors. Among them, we find that the light distribution design in the risk nodes is the more influential risk factor, so a brief design is made on them.