The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-09-06
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Chunxiao& Yue, Junjie. 2012. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression. Journal of Applied Mathematics،Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-1028948
Modern Language Association (MLA)
Zhang, Chunxiao& Yue, Junjie. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression. Journal of Applied Mathematics No. 2012 (2012), pp.1-14.
https://search.emarefa.net/detail/BIM-1028948
American Medical Association (AMA)
Zhang, Chunxiao& Yue, Junjie. The Chaotic Prediction for Aero-Engine Performance Parameters Based on Nonlinear PLS Regression. Journal of Applied Mathematics. 2012. Vol. 2012, no. 2012, pp.1-14.
https://search.emarefa.net/detail/BIM-1028948
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1028948