Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion-Reference-Cited by-同舟云学术

Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion

Published:2023-01 Issue:2 Volume:360 Page:1454-1477
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Tao Hongfeng,Qiu Jier,Chen Yiyang,Stojanovic Vladimir,Cheng Long

Funder

Suzhou Municipal Science and Technology Bureau

Fundamental Research Funds for the Central Universities

Higher Education Discipline Innovation Project

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference41 articles.

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4. Deep residual learning-based fault diagnosis method for rotating machinery;Zhang;ISA Trans.,2019

5. Artificial intelligence for fault diagnosis of rotating machinery: a review;Liu;Mech. Syst. Signal Process.,2018

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