A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions-Reference-Cited by-同舟云学术

A novel hierarchical transferable network for rolling bearing fault diagnosis under variable working conditions

Published:2023-04-04 Issue:12 Volume:111 Page:11315-11334
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Weng Chaoyang^ORCID,Lu Baochun^ORCID,Gu Qian,Zhao Xiaoli^ORCID

Funder

Key Technologies Research and Development Program

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11071-023-08405-x.pdf

Reference50 articles.

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2. Henriquez, P., Alonso, J.B., Ferrer, M.A., Travieso, C.M.: Review of automatic fault diagnosis systems using audio and vibration signals. IEEE Trans. Syst. Man Cybern. Syst. 44, 642–652 (2014)

3. Liu, J., Xu, Z.D.: A simulation investigation of lubricating characteristics for a cylindrical roller bearing of a high-power gearbox. Tribol. Int. 167, 107373 (2022)

4. Liu, J., Wang, L.F., Shi, Z.F.: Dynamic modelling of the defect extension and appearance in a cylindrical roller bearing. Mech. Syst. Signal Process. 173, 109040 (2022)

5. Chen, Z., Mauricio, A., Li, W., Gryllias, K.: A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mech. Syst. Signal Process. 140, 106683 (2020)

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