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Abstract

The electro-hydraulic actuator plays a significant role in the automatic flight control system, which is critical to the flight security, so it is vital to monitor the state of health and predict the remaining useful life for the electro-hydraulic actuator. Relevance vector machine is flourishing in the field of remaining useful life prediction and gradually applied to the prediction of complex systems or components. However, the general relevance vector machine cannot achieve on-line prediction efficiently due to its high computational complexity, besides, the sparse relevance vector machine model which is only based on historical data set could cause a great prediction error in the long term. To deal with these plights, an optimized incremental learning and on-line training algorithm based on the relevance vector machine is presented taking full advantage of the on-line updating samples to improve the precision of prediction, and in order to remedy the problem of computational complexity and improve the computational efficiency, the “sample entropy” is introduced as an effective signature of the electro-hydraulic actuator’s health to effectively reduce the size of training samples. The effectiveness of the proposed on-line training approach is evaluated through an experiment of an electro-hydraulic actuator, and the results show a satisfactory prediction accuracy as well as an improvement in the computational efficiency for remaining useful life prediction.

Keywords

Electro-hydraulic actuator remaining useful life relevance vector machine prediction incremental learning sample entropy

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Remaining useful life prediction for the electro-hydraulic actuator based on improved relevance vector machine

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