A Multiscale Feature Weighted Transfer Network for Unlabeled Rotating Machinery Fault Diagnosis-Reference-Cited by-同舟云学术

A Multiscale Feature Weighted Transfer Network for Unlabeled Rotating Machinery Fault Diagnosis

Published:2024-06-15 Issue:12 Volume:24 Page:19305-19315
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Ge Liang¹^ORCID,Wang Yinjun²^ORCID,Ding Xiaoxi¹^ORCID,Huang Wenbin¹^ORCID,Chen Yujin²^ORCID,Wang Liming¹^ORCID

Affiliation:

1. State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing, China

2. State Key Laboratory of Coal Mine Disaster Prevention and Control, China Coal Technology and Engineering Group Corporation Chongqing Research Institute, Chongqing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

State Key Laboratory of Gas Disaster Detecting, Preventing and Emergency Controlling Open-Fund Project

Special Project for Foundation of Technological Innovation and Entrepreneurship from China Coal Technology & Engineering Group Corp.

Chongqing Coal Mine Safety Production Science and Technology Project of China (Yu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10558706/10500301.pdf?arnumber=10500301

Reference20 articles.

1. Variable-scale evolutionary adaptive mode denoising in the application of gearbox early fault diagnosis

2. Intelligent Rolling Bearing Fault Diagnosis via Vision ConvNet

3. A Two-Stage Transfer Adversarial Network for Intelligent Fault Diagnosis of Rotating Machinery With Multiple New Faults

4. Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery

5. Adaptive Weighted Signal Preprocessing Technique for Machine Health Monitoring

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