Akademska digitalna zbirka SLovenije - logo
(UL)
PDF
  • Multi-objective adjustment of remaining useful life predictions based on reinforcement learning [Elektronski vir]
    Kozjek, Dominik ; Malus, Andreja ; Vrabič, Rok
    Effective tracking of degradation in machine tools or vehicle, ship, and aircraft engines is key to ensure their high utilization, effective maintenance, and safety. Data from the built-in sensors ... can be used to build models that accurately predict the remaining useful life (RUL) of the observed system. However, existing approaches often lack the ability to incorporate domain-specific knowledge in form of degradation models. This paper proposes a reinforcement-learning based approach for encoding the degradation model used for multi-objective adjustment of RUL predictions. The approach is demonstrated with a case of RUL prediction for aircraft engines.
    Vir: 53rd CIRP Conference on Manufacturing Systems 2020 [Elektronski vir] : [July 1-3 at Northwestern University in Chicago, USA] (Str. 425-430)
    Vrsta gradiva - prispevek na konferenci
    Leto - 2020
    Jezik - angleški
    COBISS.SI-ID - 30188803

    Povezava(-e):

    https://www.sciencedirect.com/science/article/pii/S2212827120306582
    Repozitorij Univerze v Ljubljani – RUL
    DOI

Vnos na polico