Remaining Useful Life Prediction of Rolling Bearings Using GRU-DeepAR with Adaptive Failure Threshold

Aiming at the problem that a single neural network model has difficulty in accurately predicting trends of the remaining useful life of a rolling bearing, a method of predicting the remaining useful life of rolling bearings using a gated recurrent unit-deep autoregressive model (GRU-DeepAR) with an...

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Detalles Bibliográficos
Autores principales: Li, Jiahui, Wang, Zhihai, Liu, Xiaoqin, Feng, Zhengjiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919518/
https://www.ncbi.nlm.nih.gov/pubmed/36772183
http://dx.doi.org/10.3390/s23031144

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919518/
https://www.ncbi.nlm.nih.gov/pubmed/36772183
http://dx.doi.org/10.3390/s23031144

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