Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT

Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT

In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol sear...

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Bibliographic Details
Main Authors: Qing Chen, Sheng Zheng, Xing Wu, Tao Liu
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Applied Sciences
Subjects:
bearing early fault
feature extraction
adaptive empirical wavelet transform
Laplace wavelet dictionary
feature sign search
Online Access:https://www.mdpi.com/2076-3417/12/21/10807

Internet

https://www.mdpi.com/2076-3417/12/21/10807

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