Physics-Embedded Recurrent Graph Neural Network for Fault Diagnosis of Complex Systems-Reference-Cited by-同舟云学术

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Physics-Embedded Recurrent Graph Neural Network for Fault Diagnosis of Complex Systems

Published:2024 Issue: Volume:12 Page:122426-122436
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Gao Yushan¹^ORCID,Zhong Zhirong²^ORCID,Ma Meng³^ORCID,Zhang Zhenzhen¹^ORCID,Zhang Yuxiang³,Wang Chenxi³^ORCID,Wang Zhizhen³

Affiliation:

1. Science and Technology on Liquid Rocket Engine Laboratory, Xi’an Aerospace Propulsion Institute, Xi’an, China

2. School of Future Technology, Xi’an Jiaotong University, Xi’an, China

3. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China

Funder

National Natural Science Foundation of China

Postdoctoral Science Fund

Basic Research Program of China

Basic Research Fund of Xi’an Jiaotong University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10659814.pdf?arnumber=10659814

Reference27 articles.

1. Deep neural network approach for fault detection and diagnosis during startup transient of liquid-propellant rocket engine

2. Challenges and Opportunities of System-Level Prognostics

3. Multiple Asteroid Retrieval Mission from Lunar Orbital Platform-Gateway Using Reusable Spacecrafts

4. Research and Development of Fault Diagnosis Methods for Liquid Rocket Engines

5. A Supervised Framework for Recognition of Liquid Rocket Engine Health State Under Steady-State Process Without Fault Samples

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