NDLI: Wear trend forecast of aero-engine based on improved RBF neural network

Content Provider	IEEE Xplore Digital Library
Author	Liying Jiang Lei Wang Jianhui Xi Yibo Li Yan Zhang
Copyright Year	2010
Description	Author affiliation: College of Automation, Shenyang Institute of Aeronautical Engineering, 110136, China (Liying Jiang; Lei Wang; Jianhui Xi; Yibo Li; Yan Zhang)
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.
Starting Page	2234
Ending Page	2237
File Size	313800
Page Count	4
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498830
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wear Trend forecast RBF Neural Network Neurons Predictive models Spectra Engines Petroleum Information analysis Support vector machines Accuracy Aero-Engine Neural networks Radial basis function networks Lubrication Equal Dimensionality Vectors
Content Type	Text
Resource Type	Article

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