The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning-Reference-Cited by-同舟云学术

The fault diagnosis method of rolling bearing under variable working conditions based on deep transfer learning

Published:2020-10-19 Issue:11 Volume:42 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Dong Shaojiang,He Kun^ORCID,Tang Baoping

Funder

National Natural Science Foundation of China

Natural Science Foundation Project of CQ

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,General Engineering,Aerospace Engineering,Automotive Engineering,Industrial and Manufacturing Engineering,Applied Mathematics

Link

https://link.springer.com/content/pdf/10.1007/s40430-020-02661-3.pdf

Reference34 articles.

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2. Yang BY, Liu RN, Chen XF (2017) Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Electron 13(3):1321–1331

3. Wang TY, Liang M, Li JY (2014) Rolling element bearing fault diagnosis via fault characteristic order(FCO) analysis. Mech Syst Signal Process 45(1):139–153

4. Xu J, Zhao J, Ma B (2013) Fault diagnosis of complex industrial process using KICA and sparse SVM. Math Probl Eng 3:87–118

5. Liu HH, Han MH (2014) A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings. Mech Mach Theory 18(75):67–78

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