Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dicti...

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Bibliographic Details
Main Authors: Haodong Yuan, Nailong Wu, Xinyuan Chen, Yueying Wang
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Machines
Subjects:
shift invariant K-SVD
support vector machine
dictionary learning
fault diagnosis
rolling bearing
Online Access:https://www.mdpi.com/2075-1702/9/5/98

Internet

https://www.mdpi.com/2075-1702/9/5/98

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