Implementation method of an avionics fault diagnosis system for airborne line maintenance

Abstract

The invention discloses a method for realizing an avionics fault diagnosis system oriented to airborne route maintenance. This method uses the Bayesian network as the framework to establish a fault diagnosis model, combined with the joint tree algorithm for diagnosis and analysis, which solves the traditional limitation of low efficiency in troubleshooting by querying maintenance manuals, reduces the dependence on the actual experience of maintenance personnel, and improves The reasoning efficiency is improved; the association rules are used to mine the strong association rules of historical maintenance data, and the expert experience is used for parameter learning, which solves the traditional limitation of relying solely on expert experience and makes full use of historical maintenance data; real-time access to maintenance information of each member system, and The fault information input by maintenance personnel through the human-computer interaction window is used as observation evidence and input into the fault diagnosis model to update the posterior probability distribution in real time, realizing the dynamic diagnosis process of airborne line maintenance; improving the accuracy of fault diagnosis, It provides a certain theoretical basis for the design of the domestic airborne maintenance system.

Description

technical field [0001] The invention relates to the technical field of civil aviation airborne route maintenance, in particular to an implementation method of an avionics fault diagnosis system oriented to airborne route maintenance. Background technique [0002] The avionics system is referred to as avionics system for short, mainly including avionics systems such as navigation and communication. The structure of the avionics system is complex, there is a high degree of information interaction between each subsystem, and there is great uncertainty in the failure. After a failure, it is likely to cause failures in related structural parts. Sometimes the same failure symptom corresponds to several failure causes, and the same failure cause will cause multiple failure symptoms. There are complex forms such as related failures and multiple failures; The system is often used in an environment with complex interference factors. Even for the same type of aircraft, the types of fa...

AI Technical Summary

Problems solved by technology

[0004] 1. Technical issues: The structure of the avionics system is complex, the data is highly cross-linked, and there are complex forms such as multiple faults and related faults. One fault sometimes corresponds to multiple fault symptoms, and one fault symptom sometimes corresponds to multiple fault causes. Determining the one-to-one correspondence between failure phenomena and failure causes is a difficult problem; it is difficult for avionics systems to obtain complete maintenance data. How to effectively analyze historical maintenance data and expert experience on the basis of incomplete historical maintenance data? It is a difficult problem; historical maintenance data records are uneven, how to unify and enter them, how to quickly mine the correlation information between faults in historical maintenance data, and how to implement real-time fault diagnosis model with the increase of historical maintenance data Dynamic learning of parameters is a difficult problem

Images