Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds-Reference-Cited by-同舟云学术

Unsupervised fault diagnosis of wind turbine bearing via a deep residual deformable convolution network based on subdomain adaptation under time-varying speeds

Published:2023-02 Issue: Volume:118 Page:105656
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Liang Pengfei,Wang Bin,Jiang Guoqian,Li Na,Zhang Lijie

Funder

Natural Science Foundation of Hebei Province

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference42 articles.

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4. Approximate statistical tests for comparing supervised classification learning algorithms;Dietterich;Neural Comput.,1998

5. Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN;Gao;ISA Trans.,2021

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