Few-Shot Bearing Fault Diagnosis Via Ensembling Transformer-Based Model With Mahalanobis Distance Metric Learning From Multiscale Features-Reference-Cited by-同舟云学术

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Few-Shot Bearing Fault Diagnosis Via Ensembling Transformer-Based Model With Mahalanobis Distance Metric Learning From Multiscale Features

Published:2024 Issue: Volume:73 Page:1-18
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Vu Manh-Hung¹^ORCID,Nguyen Van-Quang¹^ORCID,Tran Thi-Thao²^ORCID,Pham Van-Truong²^ORCID,Lo Men-Tzung³^ORCID

Affiliation:

1. Department of Automation Engineering, School of Electrical and Electronic, Hanoi University of Science and Technology, Hanoi, Vietnam

2. Department of Automation Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

3. Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan

Funder

Vietnam National Foundation for Science and Technology Development

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10478558.pdf?arnumber=10478558

Reference90 articles.

1. A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

2. Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

3. Motor Fault Diagnostics Based on Current Signatures: A Review

4. Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016

5. Condition monitoring and fault diagnosis of planetary gearboxes: A review

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