Novel Extreme Learning Machine Using Kalman Filter for Performance Prediction of Aircraft Engine in Dynamic Behavior
Publication: Journal of Aerospace Engineering
Volume 33, Issue 5
Abstract
In this paper, a novel state-propagation extreme learning machine using a Kalman filter (KF-ELM) is proposed. In comparison with the plain extreme learning machine, the proposed algorithm takes the topological parameters as state variables and minimizes the covariance of state estimates to overcome the state conjunction and transformation dilemma in time series. As a result, its topological stability and prediction accuracy are enhanced, and these merits are further proved theoretically. In addition, the computational effort of KF-ELM is on the same order of magnitude as the plain extreme learning machine, while the former possesses a faster convergent speed. Then, several benchmark datasets are utilized to test the effectiveness and soundness of the proposed algorithm. Finally, it is employed to predict the gas path performance of a turbofan engine. The performance prediction accuracy is better than the plain ELM with different input rules in the dynamic process. Particularly under various flight operation conditions, the proposed algorithm performs well and its stability is sufficiently showcased. In a word, the proposed algorithm provides a candidate technique for predicting aircraft engine performance in dynamic behavior.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We are grateful for the financial support of the National Nature Science Foundation of China (No. 91960110) and the Fundamental Research Funds for the Central Universities (No. NS2018018). Gratitude is also extended to the China Scholarship Council for supporting the first author to carry out collaborative research in the Department of Mechanical and Industrial Engineering at the University of Toronto.
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Published In
Journal of Aerospace Engineering
Volume 33 • Issue 5 • September 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Oct 24, 2018
Accepted: Mar 9, 2020
Published online: Jun 9, 2020
Published in print: Sep 1, 2020
Discussion open until: Nov 9, 2020
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