Reliability Analysis of Phased-Mission Systems Using Bayesian Networks
Yu Ding, Qin Ping Zhao, Zhixing Wei · 2023
As the aircraft/engine integrated system evolves, the aircraft's structural composition becomes increasingly complex, system coupling intensifies, and the array of tasks diversifies. Concurrently, the structural configuration, failure criteria, and causal relationships within the aircraft/engine integrated system undergo changes with shifting mission phases, necessitating phased-mission reliability modeling and analysis. Addressing this, this study introduces a phased-mission reliability analysis method for the aircraft/engine integrated system based on Bayesian networks. Initially, we delve into the integrated system's structural composition and operational principles across multiple mission phases, leading to the construction of a multi-phase fault tree. Subsequently, the correlation between the fault tree and the Bayesian network is explored, facilitating the transformation of the fault tree into a Bayesian network model suited for multi-phase missions. Ultimately, system reliability is deduced through forward inference throughout the span of multi-phase missions. Concurrently, using the Bayesian network's reverse inference mechanism, we identify the system's vulnerabilities across different mission phases. The method outlined herein offers pivotal technical support for the reliability design and analysis of aircraft/engine integrated systems.