Dynamic-scale graph neural network for fault detection-Reference-Cited by-同舟云学术

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Dynamic-scale graph neural network for fault detection

Published:2022-12 Issue: Volume:168 Page:953-970
ISSN:0957-5820
Container-title:Process Safety and Environmental Protection
language:en
Short-container-title:Process Safety and Environmental Protection

Author:

Lin Zhengqing,Hu Zhengwei,Peng Jingchao,Zhao Haitao

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering,Environmental Chemistry,Environmental Engineering

Reference39 articles.

1. A data-driven Bayesian network learning method for process fault diagnosis;Amin;Process Saf. Environ. Prot.,2021

2. An analysis of process fault diagnosis methods from safety perspectives;Arunthavanathan;Comput. Chem. Eng.,2021

3. Nonlinear dynamic process monitoring based on dynamic kernel PCA;Choi;Chem. Eng. Sci.,2004

4. Sparse kernel locality preserving projection and its application in nonlinear process fault detection;Deng;Chin. J. Chem. Eng.,2013

5. A plant-wide industrial process control problem;Downs;Comput. Chem. Eng.,1993

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2. Explainable AI methodology for understanding fault detection results during Multi-Mode operations;Chemical Engineering Science;2024-11

3. Fault detection and identification method: 3D-CNN combined with continuous wavelet transform;Computers & Chemical Engineering;2024-10

4. Fault detection using Graph Neural Differential Auto-encoders (GNDAE);Computers & Chemical Engineering;2024-10

5. Graph neural networks for anomaly detection and diagnosis in hydrogen extraction systems;Engineering Applications of Artificial Intelligence;2024-09

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