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Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics.pdf (4.759Mb)
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Izenburua
Integrated machine learning and probabilistic degradation approach for vessel electric motor prognostics
Egilea
Aizpurua Unanue, Jose Ignacio
Egilea (beste erakunde batekoa)
Knutsen, Knut Erik
Heimdal, Markus
Vanem, Erik
Ikerketa taldea
Teoría de la señal y comunicaciones
Beste instituzio
Ikerbasque
DNV
Kongsberg Maritime (Norway)
University of Oslo
Bertsioa
Postprinta
Eskubideak
© 2023 Elsevier
Sarbidea
Sarbide bahitua
URI
https://hdl.handle.net/20.500.11984/6065
Argitaratzailearen bertsioa
https://doi.org/10.1016/j.oceaneng.2023.114153
Non argitaratua
Ocean Engineering  Vol. 275. N. artículo 114153
Argitaratzailea
Elsevier
Gako-hitzak
Prognostics
Degradation
Electric motor
Insulation ... [+]
Prognostics
Degradation
Electric motor
Insulation
uncertainty
Machine learning [-]
Laburpena
In the transition towards more sustainable ships, electric motors (EM) are being used in ship propulsion systems to reduce emissions and increase efficiency. The safe operation of ships is crucial, an ... [+]
In the transition towards more sustainable ships, electric motors (EM) are being used in ship propulsion systems to reduce emissions and increase efficiency. The safe operation of ships is crucial, and prognostics and health management applications have emerged as effective solutions to transit towards monitored reliable systems. In this context, this paper presents a probabilistic EM prognostics model integrating data-driven operational models and physics-informed degradation models. Firstly, motor torque and winding temperature are estimated through connected machine learning models, which are based on operational and meteorological data. Operational and meteorological variables drive the EM degradation model and enable the analysis of EM degradation under different operational and environmental conditions. Subsequently, EM remaining useful life (RUL) is predicted within a probabilistic Monte-Carlo approach combining the thermal-stress model along with the associated uncertainties. The methodology is tested on a real case study of the OV Ryvingen vessel, with collected data during voyages along the Norwegian coast. Results confirm the validity of the designed RUL model showing that, under normal operation conditions, the degradation is mild, and the temperature measurement errors are important for RUL estimation. [-]
Sponsorship
Gobierno de España
Projectu ID
MIDAS project (grant number 282202)
Bildumak
  • Artikuluak - Ingeniaritza [743]

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