Multi-objective adjustment of remaining useful life predictions based on reinforcement learning
Kozjek, Dominik (Author), Malus, Andreja (Author), Vrabič, Rok (Author)

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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.

Keywords:predictive maintainance, remaining useful life, reinforcement learning
Work type:Article (dk_c)
Tipology:1.08 - Published Scientific Conference Contribution
Organization:FS - Faculty of Mechanical Engineering
Number of pages:Str. 425-430
ISSN on article:2212-8271
DOI:10.1016/j.procir.2020.03.051 Link is opened in a new window
COBISS.SI-ID:30188803 Link is opened in a new window
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Record is a part of a proceedings

Title:53rd CIRP Conference on Manufacturing Systems 2020
COBISS.SI-ID:30177283 This link opens in a new window

Record is a part of a journal

Title:Procedia CIRP
COBISS.SI-ID:12981019 This link opens in a new window

Document is financed by a project

Funding Programme:MIZŠ
Project no.:C3330-16-529000

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0270
Name:Proizvodni sistemi, laserske tehnologije in spajanje materialov

Secondary language

Keywords:napovedno vzdrževanje, preostala uporabna doba, vzpodbujevalno učenje

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