A Calibration-Based Hybrid Transfer Learning Framework for RUL Prediction of Rolling Bearing Across Different Machines-Reference-Cited by-同舟云学术

A Calibration-Based Hybrid Transfer Learning Framework for RUL Prediction of Rolling Bearing Across Different Machines

Published:2023 Issue: Volume:72 Page:1-15
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Deng Yafei¹^ORCID,Du Shichang¹^ORCID,Wang Dong¹^ORCID,Shao Yiping²^ORCID,Huang Delin³^ORCID

Affiliation:

1. State Key Laboratory of Mechanical System and Vibration and the Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

2. College of Mechanical Engineering, Zhejiang University of Technology, Zhejiang, China

3. College of Intelligent Manufacturing and Control Engineering, Shanghai Polytechnic University, Shanghai, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Zhejiang Provincial Natural Science Foundation of China

Ningbo City, Zhejiang Province, Major Scientific and Technological Breakthrough and “Unveiling and Commanding” Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10078406.pdf?arnumber=10078406

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5. Transfer learning for remaining useful life prediction of multi-conditions bearings based on bidirectional-GRU network

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