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Journal of Applied Mathematics
Volume 2012, Article ID 615618, 14 pages
Research Article

The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression

1College of Science, Civil Aviation University of China, Tianjin 300300, China
2Department of Mathematical Sciences, Tsinghua University, Beijing 100084, China

Received 3 May 2012; Revised 18 July 2012; Accepted 19 July 2012

Academic Editor: Zhiwei Gao

Copyright © 2012 Chunxiao Zhang and Junjie Yue. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.