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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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MDPI
2021
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| 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 |