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Remaining Useful Life Prognostics of Bearings Based on a Novel Spatial Graph-Temporal Convolution Network

As key equipment in modern industry, it is important to diagnose and predict the health status of bearings. Data-driven methods for remaining useful life (RUL) prognostics have achieved excellent performance in recent years compared to traditional methods based on physical models. In this paper, we...

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Detalles Bibliográficos
Autores principales:	Li, Peihong, Liu, Xiaozhi, Yang, Yinghua
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2021
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233814/ https://www.ncbi.nlm.nih.gov/pubmed/34205477 http://dx.doi.org/10.3390/s21124217

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