Dual-kernel driven convolutional sparse learning for bearing transient feature enhancement-Reference-Cited by-同舟云学术

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Dual-kernel driven convolutional sparse learning for bearing transient feature enhancement

Published:2023-07 Issue: Volume:216 Page:112643
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Yi Hongrui,Ding Xiaoxi^ORCID,Li Quanchang,Wang Hao,Tang Jian,Liu Rui,Huang Wenbin

Funder

Central University Basic Scientific Research Business Expenses Special Funds

Natural Science Foundation of Chongqing

National Key Research and Development Program of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference38 articles.

1. Scale independent flexible bearing health monitoring index based on time frequency manifold energy & entropy;Noman;Meas. Sci. Technol.,2020

2. Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing;Noman;Meas,2021

3. A Novel Fast Entrogram and its applications in rolling bearing fault diagnosis;Zhang;Mech. Syst. Sig. Process.,2021

4. Rolling bearing fault diagnosis using optimal ensemble deep transfer network;Li;Knowl.-Based,2021

5. Applications in bearing fault diagnosis of an improved Kurtogram algorithm based on flexible frequency slice wavelet transform filter bank;Sheng;Meas,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Application of Adaptive Lasso-Based Minimum Entropy Deconvolution for Bearing Fault Detection Based on Vibration Signal;IEEE Sensors Journal;2024-07-15

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