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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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| Pubblicato in: | Sensors (Basel) |
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| Autori principali: | , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2017
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| Soggetti: | |
| Accesso online: | 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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