Title :
Wear trend forecast of aero-engine based on improved RBF neural network
Author :
Jiang, Liying ; Wang, Lei ; Xi, Jianhui ; Li, Yibo ; Zhang, Yan
Author_Institution :
Coll. of Autom., Shenyang Inst. of Aeronaut. Eng., Shenyang, China
Abstract :
An improved RBF neural network is proposed in this paper, which is to solve the problem of wear trend prediction accuracy for aero-engine. The number of neurons in the input layer of this improved model is determined by the ideology of equal dimensionality vectors, obtain the optimal model, then the content of iron and silicon element in the spectral can be predicted by the trained model, finally wear trend of aero-engine is determined. The simulation results show that, comparing with other models, the improved RBF neural network has great practicability and satisfied prediction accuracy in the field of wear trend.
Keywords :
aerospace engines; condition monitoring; fault diagnosis; mechanical engineering computing; radial basis function networks; vectors; aeroengine trend prediction accuracy; aeroengine wear trend forecast; equal dimensionality vector; improved RBF neural network; satisfied prediction accuracy; Accuracy; Engines; Information analysis; Lubrication; Neural networks; Neurons; Petroleum; Predictive models; Radial basis function networks; Support vector machines; Aero-Engine; Equal Dimensionality Vectors; RBF Neural Network; Spectra; Wear Trend forecast;
Conference_Titel :
Control and Decision Conference (CCDC), 2010 Chinese
Conference_Location :
Xuzhou
Print_ISBN :
978-1-4244-5181-4
Electronic_ISBN :
978-1-4244-5182-1
DOI :
10.1109/CCDC.2010.5498830
