MATEC Web Conf.
Volume 277, 20192018 International Joint Conference on Metallurgical and Materials Engineering (JCMME 2018)
|Number of page(s)||7|
|Section||Data and Signal Processing|
|Published online||02 April 2019|
Remaining useful life prognostics for the electro-hydraulic actuator using relevance vector machine and optimized on-line incremental learning
School of Electronic Information and Automation, Civil Aviation University of China, No. 2898 Jin North Road, Tianjin, 300300, China
2 School of Computer science and technology,Civil Aviation University of China, No. 2898 Jin North Road, Tianjin, 300300, China
* Corresponding author: email@example.com
The electro-hydraulic actuator plays a significant role in the automatic flight control system, so it is vital to predict the remaining useful life (RUL) for the electro-hydraulic actuators. Relevance vector machine (RVM) is flourishing in the field of RUL prognostics and gradually applied to the prediction of complex systems or components, but the general RVM cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse RVM model which is only based on historical data set could cause a large prediction error in the long term. To deal with these plights, an optimized incremental learning algorithm based on RVM is presented taking full advantage of the on-line updating samples to improve the precision of prognostics.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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