Medical rolling bearing fault prognostics based on improved extreme learning machine-Reference-Cited by-同舟云学术

Medical rolling bearing fault prognostics based on improved extreme learning machine

Published:2019-11-30 Issue:4 Volume:42 Page:700-721
ISSN:1382-6905
Container-title:Journal of Combinatorial Optimization
language:en
Short-container-title:J Comb Optim

Author:

He Cheng,Liu Changchun,Wu Tao,Xu Ying,Wu Yang,Chen Tong^ORCID

Funder

Shanghai Polytechnic University Graduate Program Fund

Subject funding of Shanghai Polytechnic University

Shanghai Science and Technology Agriculture Project

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Theory and Mathematics,Control and Optimization,Discrete Mathematics and Combinatorics,Computer Science Applications

Link

https://link.springer.com/content/pdf/10.1007/s10878-019-00494-y.pdf

Reference18 articles.

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2. Chen T, He C, Wu Y (2017) Prognostics and health management of life-supporting medical instruments. J Comb Optim 37(1):1–2

3. Cong C (2013) Research on fault diagnosis method of rolling bearing based on wavelet theory. University of Electronic Science and Technology of China

4. Dinkel H (2012) Elm-the database of eukaryotic linear motifs. Nucleic Acids Res 40(9):D242–D251

5. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544

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