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Remaining Useful Life Estimation for Engineered Systems Operating under Uncertainty with Causal GraphNets
In this work, a novel approach, termed GNN-tCNN, is presented for the construction and training of Remaining Useful Life (RUL) models. The method exploits Graph Neural Networks (GNNs) and deals with the problem of efficiently learning from time series with non-equidistant observations, which may spa...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512019/ https://www.ncbi.nlm.nih.gov/pubmed/34640645 http://dx.doi.org/10.3390/s21196325 |