Multirelational Fusion Graph Convolution Network With Multiscale Residual Network for Fault Diagnosis of Complex Industrial Processes-Reference-Cited by-同舟云学术

Multirelational Fusion Graph Convolution Network With Multiscale Residual Network for Fault Diagnosis of Complex Industrial Processes

Published:2024 Issue: Volume:73 Page:1-15
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Han Yinghua¹^ORCID,Tuo Siwei¹^ORCID,Li Yuan¹^ORCID,Zhao Qiang²^ORCID

Affiliation:

1. School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

2. School of Control Engineering and the Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Northeastern University at Qinhuangdao, Qinghuangdao, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hebei Province of China

Research Fund from the State Key Laboratory of Rolling and Automation, Northeastern University

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10381773.pdf?arnumber=10381773

Reference43 articles.

1. Self-Supervised Simple Siamese Framework for Fault Diagnosis of Rotating Machinery With Unlabeled Samples

2. Fault Diagnosis of Complex Chemical Processes Based on Enhanced Naive Bayesian Method

3. Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes

4. Fast and Accurate Classification of Time Series Data Using Extended ELM: Application in Fault Diagnosis of Air Handling Units

5. Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data

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