Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

This paper presents a supervised feature extraction method called weighted kernel entropy component analysis (WKECA) for fault diagnosis of rolling bearings. The method is developed based on kernel entropy component analysis (KECA) which attempts to preserve the Renyi entropy of the data set after d...

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Detalles Bibliográficos
Autores principales: Zhou, Hongdi, Shi, Tielin, Liao, Guanglan, Xuan, Jianping, Duan, Jie, Su, Lei, He, Zhenzhi, Lai, Wuxing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375911/
https://www.ncbi.nlm.nih.gov/pubmed/28335480
http://dx.doi.org/10.3390/s17030625

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5375911/
https://www.ncbi.nlm.nih.gov/pubmed/28335480
http://dx.doi.org/10.3390/s17030625

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