In the application of Bayesian Networks (BN) for large scale engineering systems, computer memory for the network’s parameters increases exponentially with the nodes of the system. Aiming at this problem, one method, in which the traditional Conditional Probability Table (CPT) is replaced by the Probability Tree (PT) based on the Context Specific Independence (CSI), can effectively reduce the storage complexity. In view of that Aircraft failure is often expressed in a multi-input single-output logic gates manner, as well as that fault tree is a special kind of BN, the reductive method based on CSI was proposed for the application of the BNs modeled from the commonly used typical logic gates, and it proves that the storage need using PT is linear with the nodes, rather than exponential. Simultaneously, the formula for calculating the number of parameters needed in a PT is given. Finally, this new method (PT) is used to the fault diagnostic model of an aircraft nose wheel steering system, in comparison with the original CPT method, which shows that PT approach can effectively reduce computer memory needs.