NDLI: Condition monitoring of the aircraft airborne equipment based on neural network and information fusion

Content Provider	IEEE Xplore Digital Library
Author	Jianguo Cui Xue Yan Liying Jiang Yiwen Qi Yunzhe An Dong Zhang
Copyright Year	2013
Description	Author affiliation: Sch. of Comput., Shenyang Aerosp. Univ., Shenyang, China (Yunzhe An) \|\| Sch. of Autom., Shenyang Aerosp. Univ., Shenyang, China (Jianguo Cui; Xue Yan; Liying Jiang; Yiwen Qi) \|\| Shenyang Aeroengine Res. Inst., Shenyang, China (Dong Zhang)
Abstract	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.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	1108
Ending Page	1112
File Size	385250
Page Count	5
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561090
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Condition monitoring Accuracy Uncertainty Condition Monitoring Aircraft Airborne Equipment Aero-engine Lubrication System Lubrication Information Fusion Aircraft Biological neural networks
Content Type	Text
Resource Type	Article

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