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
Link To Document :
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