A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis-Reference-Cited by-同舟云学术

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A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis

Published:2019-09 Issue: Volume:110 Page:36-47
ISSN:0166-3615
Container-title:Computers in Industry
language:en
Short-container-title:Computers in Industry

Author:

Li Xin,Yang Yu,Pan Haiyang,Cheng Jian,Cheng Junsheng

Funder

National Natural Science Foundation of China

Key Research and Development Program of Hunan Province

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference38 articles.

1. Rolling bearing fault diagnosis and performance degradation assessment under variable operation conditions based on nuisance attribute projection;Huang;Mech. Syst. Signal Process.,2019

2. Bearing performance degradation assessment using long short-term memory recurrent network;Zhang;Comput. Ind.,2019

3. Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings;Zeng;Mech. Syst. Signal Process.,2016

4. Research on bearing fault feature extraction based on singular value decomposition and optimized frequency band entropy;Li;Mech. Syst. Signal Process.,2019

5. Enhanced sparse Period-Group Lasso for bearing fault diagnosis;Zhao;IEEE Trans. Ind. Electron.,2019

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