Dynamic Fault Tree Model of Civil Aircraft Avionics Network Transmission Failure Based on Optimized Extended Fuzzy Algorithm

Abstract

The avionics network supports high-safety-level flight operations, with the analysis of transmission failures serving as a crucial means for its safety evaluation. Due to the time-dependent nature of the failure probability in avionics networks, traditional constant and unchangeable probability values can deviate from the actual situation under specific conditions. This deviation may lead to inadequate responses to occasional events and potentially cause flight accidents. A Dynamic Fault Tree (DFT) model for civil aircraft avionics network transmission failures, based on an optimized extended fuzzy algorithm, is introduced in this paper. Initially focusing on event correlations, a DFT is established for the transmission failure of the Avionics Full Duplex Switched Ethernet (AFDX). Subsequently, considering the variations between events, triangular fuzzy processing is applied to the event failure rates based on relative confidence levels. Finally, by optimizing the weakest t-norm operator, the failure probability intervals are aggregated and the fuzzy scale is regulated. Experimental results demonstrate that, compared to the static-minimum t-norm and traditional weakest t-norm methods, the proposed approach enhances the accuracy of the fuzzy failure probability intervals by 66.15% and 40.59%, respectively. Concurrently, it maintains consistency in the ranking of event importance, highlighting the superior effectiveness of the proposed method in analyzing transmission failures in avionics networks.