Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis-Reference-Cited by-同舟云学术

Dual Structural Consistent Partial Domain Adaptation Network for Intelligent Machinery Fault Diagnosis

Published:2024 Issue: Volume:73 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Yu Kun¹^ORCID,Wang Xuesong¹^ORCID,Cheng Yuhu¹^ORCID,Feng Ke²^ORCID,Zhang Yongchao³^ORCID,Xing Bin⁴^ORCID

Affiliation:

1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China

2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, Shanxi, China

3. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China

4. National Engineering Laboratory for Industrial Big-Data Application Technology, Chongqing Innovation Center of Industrial Big-Data Company Ltd., Chongqing, China

Funder

National Natural Science Foundation

Natural Science Foundation of Jiangsu Province

China Postdoctoral Science Foundation

Chongqing Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10520333.pdf?arnumber=10520333

Reference37 articles.

1. Applications of machine learning to machine fault diagnosis: A review and roadmap

2. Early performance degradation of ceramic bearings by a twin-driven model

3. A review for control theory and condition monitoring on construction robots

4. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

5. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning