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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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| Publicado no: | Sensors (Basel) |
|---|---|
| Main Authors: | , , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
MDPI
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5375911/ https://ncbi.nlm.nih.gov/pubmed/28335480 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s17030625 |
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