Title :
Life prediction method for aircraft key component based on fuzzy integral
Author :
Cui, Jianguo ; Zhao, Wei ; Chen, Xicheng ; Jiang, Liying ; Li, Zhonghai ; Dai, Zishen
Author_Institution :
Sch. of Autom., Shenyang Aerosp. Univ., Shenyang, China
Abstract :
At present, life prediction for the aircraft key component is a widely recognized problem in the field of aerospace, especially the prediction precision. From the basic concepts and theory of fuzzy integral, this paper proposes a new method of life prediction which is based on information fusion theory. Firstly, BP neural network and RBF neural network are used to predict the remaining service life of the aircraft key component respectively. On this basis, fuzzy integral theory is used to conduct a decision-level fusion of the results to the above two neural networks. The experiment results show that the method of life prediction with fuzzy integral can realize the life prediction for the aircraft key component. The prediction precision is improved after information fusion. It has a wide application prospect and great practical value.
Keywords :
aerospace components; aircraft; backpropagation; fuzzy set theory; radial basis function networks; remaining life assessment; BP neural network; RBF neural network; aerospace components; aircraft key component; backpropagation; decision-level fusion; fuzzy integral theory; information fusion theory; life prediction method; prediction precision improvement; radial basis function networks; remaining service life; Aircraft; Biological neural networks; Educational institutions; Measurement uncertainty; Prediction algorithms; Training; BP Neural Network; Fuzzy Integral; Life Prediction; RBF Neural Network;
Conference_Titel :
Control and Decision Conference (CCDC), 2012 24th Chinese
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4577-2073-4
DOI :
10.1109/CCDC.2012.6244280
