Condition Monitoring of the Aircraft Airborne Equipment Based on Neural Network and Information Fusion

According to the poor effect of current aircraft airborne equipment, a method based on the Neural Network and Dempster-Shafer evidence theory information fusion is put forward. As the important aircraft airborne equipment, the aero-engine, whose lubrication system works properly or not, directly affect the operation of the aero-engine condition. This paper will study on the condition monitoring of lubrication system of aero-engine. Firstly, through the aero-engine lubrication condition monitoring professional system, the performance status information will be got. Then given to the large amount of information we acquired, two neural networks are used to diagnose respectively. In order to improve the accuracy of the health condition monitoring, on this basis, Dempster-Shafer evidence theory is used to conduct a information fusion of the results to the above two neural networks. The advantages of the method of artificial neural network and D-S evidence theory are effectively improved the diagnostic accuracy. So this paper gives a good health diagnosis method, and it has a good value of engineering application.