Fault Inference and Analysis of Hydrogen-Electric Coupled Systems Based on Bayesian Networks

In this paper, we build a fault diagnosis model based on Bayesian network for the key equipment faults in the hydrogen electric coupling system. By analyzing the accident data of key hydrogen-related equipment, the common faults of the equipment are summarized and the different effects on the overall system performance are discussed. The model was solved using Netica software, which verified the rationality and accuracy of the proposed Bayesian network model. The results show that the model performs well in handling uncertainty and causal inference in complex systems, and can dynamically update the Bayesian network model to accurately locate the root cause of failure, and can be effectively used for fault diagnosis and risk assessment of hydrogen electric coupling system.