A fault propagation path identification method for A320 air conditioning system based on improved Bayesian network

The air conditioning system of airplanes is one of the critical airplane-mounted systems used to control the internal environments. It is highly complex in structure, having multiple closed loops and much redundancy, which causes the faults to spread among its components. The Bayesian network can be used to deduce the fault transmission path of open-looped systems, but it is unfit for close-looped structures. In order to address this issue, an enhanced model based on the Bayesian network for determining the fault transmission path of the air conditioning system in A320 aircraft in a particular structure is presented in this research. Firstly, the functional behavior and physical structure of this air conditioning system are analyzed. By using the complex network theory, a topologically directed graph of this system is constructed. Additionally, in order to quantify the relevance of sides, the intensity of side influence is established based on the network structure and message transit, which increases the measurement’s accuracy.Then, through the loop-opening strategy based on the intensity of side influence, a close-looped structure is changed into an open-looped structure. In this way, the optimal Bayesian fault transmission network structure is obtained to precisely identify the path of fault transmission in a close-looped structure. Finally, a case study is conducted on the A320 air conditioning system to validate the proposed model.