Abstract
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.
Highlights
Electro-hydraulic actuator is a core component in the Automatic Flight Control System (AFCS) of civil aircraft
This paper presents an optimized on-line incremental learning algorithm based on Relevance vector machine (RVM) to realize electrohydraulic actuator’s remaining useful life (RUL) prognostics
In order to further verify the performance of the proposed on-line RUL prognostics algorithm, we conduct an experiment against the internal leakage fault of an electrohydraulic actuator
Summary
Electro-hydraulic actuator is a core component in the Automatic Flight Control System (AFCS) of civil aircraft. Model-based approaches like Kalman filtering (KF) and particle filtering (PF) typically involve the establishment of a physical model to describe the mechanism of the system degradation and failure evolvement. They may not be suitable for many practical applications [3]. The RVM algorithm equipped with the characteristic of small sample size still has no efficacious on-line training technology that can be actualized to process online modelling and prognostics [6]. The RVM algorithm is applied to predict the RUL of electro-hydraulic actuator by capturing the correspondence between the hydraulic difference and sample entropy, in addition, the uncertainty of the predictive results is characterized
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
