Remaining Useful Life Estimation for Rolling Bearings Using MSGCNN-TR-Reference-Cited by-同舟云学术

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Remaining Useful Life Estimation for Rolling Bearings Using MSGCNN-TR

Published:2022-12-15 Issue:24 Volume:22 Page:24333-24343
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Guo Dong¹,Cao Zhi²^ORCID,Fu Hongyong³^ORCID,Li Zhenxiang³^ORCID

Affiliation:

1. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, China

2. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China

3. Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China

Funder

Key Laboratory of Space Utilization, Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/9984967/09954366.pdf?arnumber=9954366

Reference40 articles.

1. Performance degradation model and prediction method of real-time remaining life for wind turbine bearings;hu;Proc CSEE,2017

2. Degradation-Aware Remaining Useful Life Prediction With LSTM Autoencoder

3. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

4. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

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1. Res-TCEANet: An expansive attention mechanism with positional correspondence based on semi-supervised temporal convolutional network for RUL estimation;Measurement;2025-02

2. A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery;Advanced Engineering Informatics;2024-08

3. A multiple conditions dual inputs attention network remaining useful life prediction method;Engineering Applications of Artificial Intelligence;2024-07

4. From Innovation to Standardization—A Century of Rolling Bearing Life Formula;Machines;2024-06-27

5. Advancements in bearing remaining useful life prediction methods: a comprehensive review;Measurement Science and Technology;2024-06-10

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