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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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Dettagli Bibliografici
Pubblicato in:Sensors (Basel)
Autori principali: Zhou, Hongdi, Shi, Tielin, Liao, Guanglan, Xuan, Jianping, Duan, Jie, Su, Lei, He, Zhenzhi, Lai, Wuxing
Natura: Artigo
Lingua:Inglês
Pubblicazione: MDPI 2017
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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