Remaining Useful Life Prediction Based on Adaptive SHRINKAGE Processing and Temporal Convolutional Network

The remaining useful life (RUL) prediction is important for improving the safety, supportability, maintainability, and reliability of modern industrial equipment. The traditional data-driven rolling bearing RUL prediction methods require a substantial amount of prior knowledge to extract degraded fe...

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Detalles Bibliográficos
Autores principales: Wang, Haitao, Yang, Jie, Shi, Lichen, Wang, Ruihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741091/
https://www.ncbi.nlm.nih.gov/pubmed/36501790
http://dx.doi.org/10.3390/s22239088

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741091/
https://www.ncbi.nlm.nih.gov/pubmed/36501790
http://dx.doi.org/10.3390/s22239088

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