A Graph Convolutional Shrinkage Network-based Fault Diagnosis Method for Industrial Process
Author:
Affiliation:
1. College of Information Science & Technology, Beijing University of Chemical Technology,Beijing,China,100029
2. Macao Polytechnic University,Faculty of Applied Sciences,Macao SAR,P.R. China,999078
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10165684/10165785/10165809.pdf?arnumber=10165809
Reference15 articles.
1. Deep Residual Shrinkage Networks for Fault Diagnosis
2. A set of new Chebyshev kernel functions for support vector machine pattern classification
3. A novel AdaBoost ensemble model based on the reconstruction of local tangent space alignment and its application to multiple faults recognition
4. Locality preserving dense graph convolutional networks with graph context-aware node representations
5. Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander's overlap coefficient
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1. A Hybrid MTS Anomaly Detection Method Based on Reconstruction and Adaptive Spatial-Temporal Graph Network;2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS);2024-05-17
2. Deep Graph Convolutional Neural Network for Fault Diagnosis of Complex Industrial Processes;2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR);2023-12-08
3. Research on Industrial Process Fault Diagnosis Based on Deep Spatio-Temporal Fusion Graph Convolutional Network;2023
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