Risk analysis and assessment of underwater glider mission failure using Dynamic Bayesian Network method

The Autonomous Underwater Glider (AUG), with its extended operational endurance, encounters various uncertain risks during diverse mission tasks. Therefore, conducting dynamic risk analysis is crucial for implementing subsequent decisions and improving equipment safety. This paper combines Fault Tree Analysis (FTA) and Dynamic Bayesian Network (DBN) to develop a customized dynamic risk assessment method for AUG task failure under multiple risk factors. Firstly, preliminary risk factors are identified by FTA using existing experimental data. Next, transformation rules are constructed to map FTA into a basic DBN model. Then, parameters such as conditional probabilities are determined through logic gates and parameter learning, and the proposed model is validated based on axioms. Finally, key risk factors are identified based on actual failure data. The research results indicate that the key risk factors leading to AUG task failure are progressive abnormalities such as biofouling and ocean current disturbance. The method proposed in this paper can achieve dynamic risk assessment, providing strong references for operators in making decisions for subsequent tasks.