Remaining Useful Life Prediction Method Based on the Spatiotemporal Graph and GCN Nested Parallel Route Model-Reference-Cited by-同舟云学术

Remaining Useful Life Prediction Method Based on the Spatiotemporal Graph and GCN Nested Parallel Route Model

Published:2024 Issue: Volume:73 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Song Liuyang¹^ORCID,Jin Ye²^ORCID,Lin Tianjiao³^ORCID,Zhao Shengkai²^ORCID,Wei Zhicheng⁴^ORCID,Wang Huaqing¹^ORCID

Affiliation:

1. College of Mechanical and Electrical Engineering and the State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing, China

2. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing, China

3. College of Mechanical and Electrical Engineering and Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-End Mechanical Equipment, Beijing University of Chemical Technology, Beijing, China

4. Chongqing Rail Transit (Group) Company Ltd., Chongqing, China

Funder

National Key Research and Development Plan of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10445763.pdf?arnumber=10445763

Reference32 articles.

1. A novel prediction network for remaining useful life of rotating machinery

2. Continual learning fault diagnosis: A dual-branch adaptive aggregation residual network for fault diagnosis with machine increments

3. Orthogonal On-Rotor Sensing Vibrations for Condition Monitoring of Rotating Machines