A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis-Reference-Cited by-同舟云学术

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A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis

Published:2022-03 Issue: Volume:191 Page:110752
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Wan Lanjun^ORCID,Li Yuanyuan,Chen Keyu,Gong Kun,Li Changyun

Funder

Scientific Research Foundation of Hunan Provincial Education Department

Key Project of Research and Development Plan of Hunan Province

National Natural Science Foundation of China

Hunan Provincial Natural Science Foundation

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference47 articles.

1. Applications of machine learning to machine fault diagnosis: A review and roadmap;Lei;Mech. Syst. Signal Process.,2020

2. Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery;Han;Trans. Inst. Meas. Control,2018

3. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network;Ali;Mech. Syst. Signal Process.,2015

4. An end-to-end model based on improved adaptive deep belief network and its application to bearing fault diagnosis;Xie;IEEE Access,2018

5. Convolutional neural network in intelligent fault diagnosis toward rotatory machinery;Tang;IEEE Access,2020

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