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A Machine Learning Model of Electro-Hydrostatic Actuators With the Low-Data Limit and Its Application to Fault Detection

This paper presents a novel approach for modeling the dynamics of electro-hydrostatic actuators (EHAs) using the sparse identification of nonlinear dynamics (SINDy) algorithm. SINDy is a machine learning algorithm that identifies governing equations from input-output data. It is a suitable algorithm...

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Autori principali: Soleiman Hosseinpour, Witold Kinsner, Nariman Sepehri
Natura: Artigo
Lingua:Inglês
Pubblicazione: IEEE 2025-01-01
Serie:IEEE Access
Soggetti:
Electro-hydrostatic actuator
SINDy algorithm
machine learning
Bayesian optimization
fault detection
Accesso online:https://ieeexplore.ieee.org/document/11272114/
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