Data-Driven Defrost-on-Demand Scheduling in Reefer Ships: A Predictive Maintenance Framework Using Real-World Sensor Data

Defrost cycles in shipboard refrigeration plants are typically initiated on fixed schedules or by operator judgement, which can lead to unnecessary energy use and temperature variability during the transport of frozen products. This study proposes a data-driven predictive maintenance approach to tri...

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Hlavní autoři:	Edurne Arriola-Gutierrez, David Boullosa-Falces, Juan L. Larrabe-Barrena
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2026-01-01
Edice:	Journal of Marine Science and Engineering
Témata:	big data defrost-on-demand data-driven marine systems predictive maintenance reefer ship supervised machine learning
On-line přístup:	https://www.mdpi.com/2077-1312/14/3/260
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