Proactive engine maintenance in aviation using data-driven remaining useful life estimation techniques

Abstract The precise forecasting of the Remaining Useful Life (RUL, expressed in hours) of aircraft engine-like systems using a synthetic dataset designed to mimic real engine degradation patterns is a key factor in setting up maintenance schedules that are cost-efficient and safety-enhancing. Tradi...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Yankai Bi, Chao Wang, Yuan Chi
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	SpringerOpen 2026-04-01
Sarja:	Journal of Engineering and Applied Science
Aiheet:	Genetic algorithm Particle swarm optimization Decision tree regression Random forest regression Light gradient boosting regression Remaining useful life
Linkit:	https://doi.org/10.1186/s44147-026-01021-7
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