Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning...

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Detalles Bibliográficos
Autores principales: Yi, Mingxiu, Zhou, Chengjiang, Yang, Limiao, Yang, Jintao, Tang, Tong, Jia, Yunhua, Yuan, Xuyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688966/
https://www.ncbi.nlm.nih.gov/pubmed/36421551
http://dx.doi.org/10.3390/e24111696

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9688966/
https://www.ncbi.nlm.nih.gov/pubmed/36421551
http://dx.doi.org/10.3390/e24111696

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