Getting a Better Sense of Data Drift in Dynamic Systems: Sequence-Based Deep Learning for Monitoring Slowly Evolving Degradation Processes

Deep Learning (DL) for monitoring slowly evolving degradation processes typically involves overcoming data drift, complexity, and unavailability issues resulting from dynamic and harsh conditions and the rarity of labeled failure patterns, respectively. While degradation patterns are mostly hidden i...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Tarek Berghout, Mohamed Benbouzid
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2023-11-01
Sarja:	Engineering Proceedings
Aiheet:	bearing deep learning degradation prognostics and health management remaining useful life sequential data
Linkit:	https://www.mdpi.com/2673-4591/58/1/70
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