A Novel Remaining Useful Life Transfer Prediction Method of Rolling Bearings Based on Working Conditions Common Benchmark-Reference-Cited by-同舟云学术

A Novel Remaining Useful Life Transfer Prediction Method of Rolling Bearings Based on Working Conditions Common Benchmark

Published:2022 Issue: Volume:71 Page:1-9
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Li Zhixuan¹^ORCID,Zhang Kai¹^ORCID,Liu Yongzhi²^ORCID,Zou Yisheng¹^ORCID,Ding Guofu¹^ORCID

Affiliation:

1. School of Mechanical Engineering and the Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Southwest Jiaotong University, Chengdu, China

2. School of Mining and Mechanical Engineering, Liupanshui Normal University, Liupanshui, China

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09912358.pdf?arnumber=9912358

Reference31 articles.

1. An Interpretable Deep Transfer Learning-Based Remaining Useful Life Prediction Approach for Bearings With Selective Degradation Knowledge Fusion

2. (WGAN) Wasserstein generative adversarial network Junhong Huang;arjovsky;Proc ICML,2017

3. A novel method for journal bearing degradation evaluation and remaining useful life prediction under different working conditions

4. Deep Dynamic Adaptive Transfer Network for Rolling Bearing Fault Diagnosis With Considering Cross-Machine Instance

5. Remaining useful life estimation using deep metric transfer learning for kernel regression

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