Slow-Varying Dynamics-Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation-Reference-Cited by-同舟云学术

Slow-Varying Dynamics-Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation

Published:2023-01 Issue:1 Volume:53 Page:592-606
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Qin Yan¹^ORCID,Yuen Chau¹^ORCID,Shao Yimin²^ORCID,Qin Bo³,Li Xiaoli⁴^ORCID

Affiliation:

1. Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore

2. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, China

3. School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou, China

4. Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR),, Singapore

Funder

A*STAR-NTUSUTD Joint Research Grant on Artificial Intelligence Partnership

Singapore Ministry of National Development and the National Research Foundation, Prime Minister’s Office under the Cities of Tomorrow (CoT) Research Programme

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/9998320/09762891.pdf?arnumber=9762891

Reference31 articles.

1. Long Short-Term Memory Network for Remaining Useful Life estimation

2. Remaining useful life estimation in prognostics using deep convolution neural networks

3. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks

4. A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings

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