Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis

Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis

For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, suc...

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Bibliographic Details
Main Authors: Lin Liang, Xingyun Ding, Fei Liu, Yuanming Chen, Haobin Wen
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
sparse kernel nonnegative matrix factorization
time–frequency distribution
rolling element bearing
feature extraction
Online Access:https://www.mdpi.com/1424-8220/21/11/3680

Internet

https://www.mdpi.com/1424-8220/21/11/3680

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