Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network

Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. M...

Descripción completa

Detalles Bibliográficos
Autores principales: Yan, Jialin, Kan, Jiangming, Luo, Haifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145222/
https://www.ncbi.nlm.nih.gov/pubmed/35632345
http://dx.doi.org/10.3390/s22103936

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145222/
https://www.ncbi.nlm.nih.gov/pubmed/35632345
http://dx.doi.org/10.3390/s22103936

Ejemplares similares