A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling Bearing Based on Ensemble Empirical Mode Decomposition | Zendy

Xiao Yu | Zendy; Bing Xia | Zendy; Shuxin Yang | Zendy; Hongshen Yin | Zendy; Yajie Wang | Zendy; Xiaowen Liu | Zendy

Open Access

A Deep Domain-Adversarial Transfer Fault Diagnosis Method for Rolling Bearing Based on Ensemble Empirical Mode Decomposition

Author(s) -

Xiao Yu,

Bing Xia,

Shuxin Yang,

Hongshen Yin,

Yajie Wang,

Xiaowen Liu

Publication year - 2022

Publication title -

journal of sensors

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.399

H-Index - 43

eISSN - 1687-7268

pISSN - 1687-725X

DOI - 10.1155/2022/8959185

Subject(s) - hilbert–huang transform , fault (geology) , artificial intelligence , computer science , feature (linguistics) , deep learning , bearing (navigation) , pattern recognition (psychology) , domain (mathematical analysis) , frequency domain , transfer of learning , time domain , domain adaptation , stability (learning theory) , algorithm , machine learning , computer vision , mathematics , mathematical analysis , linguistics , philosophy , filter (signal processing) , seismology , geology , classifier (uml)

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