A fault propagation model based on Bayesian network

Abstract

Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the dual strategy ant colony optimization algorithm (DS-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM) and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, DS-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN’s structure. Experimental results illustrate the effectiveness of the proposed model.