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2012 The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression
Chunxiao Zhang, Junjie Yue
J. Appl. Math. 2012(SI10): 1-14 (2012). DOI: 10.1155/2012/615618

Abstract

The prediction of the aero-engine performance parameters is very important for aero-engine condition monitoring and fault diagnosis. In this paper, the chaotic phase space of engine exhaust temperature (EGT) time series which come from actual air-borne ACARS data is reconstructed through selecting some suitable nearby points. The partial least square (PLS) based on the cubic spline function or the kernel function transformation is adopted to obtain chaotic predictive function of EGT series. The experiment results indicate that the proposed PLS chaotic prediction algorithm based on biweight kernel function transformation has significant advantage in overcoming multicollinearity of the independent variables and solve the stability of regression model. Our predictive NMSE is 16.5 percent less than that of the traditional linear least squares (OLS) method and 10.38 percent less than that of the linear PLS approach. At the same time, the forecast error is less than that of nonlinear PLS algorithm through bootstrap test screening.

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Chunxiao Zhang. Junjie Yue. "The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression." J. Appl. Math. 2012 (SI10) 1 - 14, 2012. https://doi.org/10.1155/2012/615618

Information

Published: 2012
First available in Project Euclid: 3 January 2013

zbMATH: 1251.93091
Digital Object Identifier: 10.1155/2012/615618

Rights: Copyright © 2012 Hindawi

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Vol.2012 • No. SI10 • 2012
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