Fault state evaluation of armored vehicle power system based on BN AHP

DOI:	10.11809/bqzbgcxb2024.03.011
Keywords:	Bayesian network; analytic hierarchy process; cloud model transformation; fault state assessment; evidence theory; expert experience
Abstract:	Whether the damage of complex traditional components of armored vehicles can be detected in time is related to the combat readiness or combat capability of the whole vehicle, and the fault state assessment of the system is crucial. In this paper, based on the Bayesian network, the cloud model theory is fused to establish the cloud Bayesian network model, which is used to evaluate the fault status of armored vehicles under four different working conditions. When obtaining the initial nodes of Bayesian network, we rely more on the experience of experts, which often leads to large error, resulting in too large deviation of conditional probability. In this paper, we use evidence theory/analytic hierarchy process to optimize the expert experience and determine the conditional probability of each node. The conditional probability value converted by the analytic hierarchy process is substituted into the Yun Baier network model, and the probability of different damage levels can be obtained through calculation. Comparing the results of the cloud Bayesian network model with the results of other state assessment methods, the results show that the calculation method used in this paper has improved reliability and accuracy compared with other methods.
Published:	2024-03-28
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