Optimization-based improved kernel extreme learning machine for rolling bearing fault diagnosis-Reference-Cited by-同舟云学术

Optimization-based improved kernel extreme learning machine for rolling bearing fault diagnosis

Published:2019-10-24 Issue:11 Volume:41 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:

Zheng Longkui,Xiang Yang,Sheng Chenxing

Funder

NSFC-Zhejiang Joint Found for the Integration of Industrialization and informatizatio

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

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

Link

http://link.springer.com/content/pdf/10.1007/s40430-019-2011-5.pdf

Reference35 articles.

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2. Gao JX, Yang JH, Huang DW, Liu HG, Liu SY (2019) Experimental application of vibrational resonance on bearing fault diagnosis. J Braz Soc Mech Sci Eng 6:1–13

3. Yan XA, Jia MP (2019) Improved singular spectrum decomposition-based 1.5-dimensional energy spectrum for rotating machinery fault diagnosis. J Braz Soc Mech Sci Eng 50:1–20

4. Lei YG, Lin J, He ZJ, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35:108–126

5. Lei YG, He ZJ, Zi YY (2009) Application of an intelligent classification method to mechanical fault diagnosis. Expert Syst Appl 36:9941–9948

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