Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous fea...

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Detalles Bibliográficos
Autores principales: Chu, Yan, Ali, Syed Muhammad, Lu, Mingfeng, Zhang, Yanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453404/
https://www.ncbi.nlm.nih.gov/pubmed/37628225
http://dx.doi.org/10.3390/e25081194

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453404/
https://www.ncbi.nlm.nih.gov/pubmed/37628225
http://dx.doi.org/10.3390/e25081194

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