Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings

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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Bibliographic Details
Main Authors: Hongdi Zhou, Tielin Shi, Guanglan Liao, Jianping Xuan, Jie Duan, Lei Su, Zhenzhi He, Wuxing Lai
Format: Article
Language:English
Published: MDPI AG 2017-03-01
Series:Sensors
Subjects:
fault diagnosis
weighted kernel entropy component analysis
dimensional reduction
Renyi entropy
feature extraction
Online Access:http://www.mdpi.com/1424-8220/17/3/625

Internet

http://www.mdpi.com/1424-8220/17/3/625

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