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Forecasting model of engine life on wing based on LS-SVM and Bayesian inference


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Forecasting model of engine life on wing based on LS-SVM and Bayesian inference
Wang YeZuo HongfuCai JingRong Xiang
College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
Bayesian inference least squares support vector machine engine life on wing prediction
V235; V37
To resolve the problem of engine life forecasting accuracy,a nonlinear forecasting model for engine life on wing is established by applying Bayesian inference to the choices of model parameters of least squares support vector machine(LS-SVM).The performance parameters affecting engine life on wing are analyzed,a forecasting model training set for machine study is established,and a forecasting model of engine life on wing is established based on the LS-SVM.The LS-SVM model is optimized by using Bayesian inference,and the best modeling parameters are obtained.The engine life on wing is forecasted by using a data set training model of a certain engine life on wing.Compared with several common algorithms,the forecasting accuracies of the model proposed here increase by 4.58%-9.51%,which solves the problem of forecasting of small samples,and performs well in generalization ability and forecasting precision.


[1] Vittal S,Hajela P,Joshi A.Review of approaches to gas turbine life management[A].10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference[C].New York,USA:American Institute for Aeronautics & Astronautics,2004:1-11.
[2]Wang W B.A model to predict the residual life of aircraft engines based upon oil analysis data[J].Naval Research Logistics,2005,52(3):276-284.
[3]Xu D,Zhao W B.Reliability prediction using multivariate degradation data[A].Proceedings of the Annual Reliability and Maintainability Symposium[C].Virginia,USA:IEEE,2005:337-341.
Tan Dongning,Tan Donghan.Small-sample machine learning theory:statistical learning theory[J].Journal of Nanjing University of Science and Technology,2001,25(1):108-112.
[5]Vapnik V N.The nature of statistical learning theory[M].New York,USA:Springer,1999.
Yu Zhengtao,Zou Junjie,Zhao Xing,et al.Sparseness of least squares support vector machines based on active learning[J].Journal of Nanjing University of Science and Technology,2012,36(1):12-17.
[7]Suykens J A K.Least squares support vector machines for classification and nonlinear modeling[J].Neural Network World,2000,10(1):29-48.
[8]Nello C,John S.An introduction to support vector machines and other kernel-based learning methods[M].Beijing:Publishing House of Electronics Industry,2004.
[9]Kowk J T.The evidence framework applied to support vector machines[J].IEEE Trans on Neural Network,2009,11(5):1162-1173.
[10]MacKay D J C.Bayesian interpolation[J].Neural Computation,1992,4(3):415-447.
[11]Robinson M E,Crowder M J.Bayesian methods for a growth-curve degradation model with repeated measures[J].Lifetime Data Analysis,2000(6):357-374.
Xing Yongzhong,Wu Xiaobei,Xu Zhiliang.Adaptive iterative LS-SVM regression algorithm based on vector base learning[J].Journal of Nanjing University of Science and Technology,2011,35(3):328-333.
[13]University of California Irvine[EB/OL].http://archive.ics.uci.edu/ml/index.html,2011-05-01.


Last Update: 2013-12-31