DocumentCode
2671328
Title
The condition trend analysis of aircraft key components based on D-S evidence theory
Author
Cui, Jianguo ; Shi, Jianqiang ; Dong, Shiliang ; Jiang, Liying ; Lv, Rui ; Liu, Haigang
Author_Institution
Sch. of Autom., Shenyang Aerosp. Univ., Shenyang, China
fYear
2012
fDate
23-25 May 2012
Firstpage
2264
Lastpage
2269
Abstract
In order to improve and heighten the accuracy of condition trend analysis to key components of aircraft, to grasp their running state in time and avoid accidents, In the beginning, the paper analyze a lot of characteristic dates of running state from a large number of long-term tests deeply. On this basis, two condition trend analysis models: GM(1,1) and ARMA model are established, using these two models to analyze the condition trend of key components of aircraft, and operating the decision-level fusion of the results of the above models with D-S evidence theory. The research shows that both of GM(1, 1) model and ARMA model can predict the condition trend of key components of aircraft, and we can get the better result after using D-S evidence theory fusion. So this paper gives a good trend analysis method, and it has a good value of engineering application.
Keywords
accident prevention; aerospace components; aircraft testing; autoregressive moving average processes; condition monitoring; decision making; grey systems; inference mechanisms; sensor fusion; uncertainty handling; ARMA model; D-S evidence theory fusion; GM(1,1) model; accident avoidance; aircraft key component; characteristic running state dates; condition trend analysis; decision level fusion; grey prediction model; long-term tests; Aircraft; Aircraft propulsion; Analytical models; Atmospheric modeling; Data models; Mathematical model; Predictive models; ARMA (n,m); Condition Trend Analysis; D-S Evidence Theory; GM (1,1); Key Components of Aircraft;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location
Taiyuan
Print_ISBN
978-1-4577-2073-4
Type
conf
DOI
10.1109/CCDC.2012.6244363
Filename
6244363
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