Improved fault detection and diagnosis using graph auto encoder and attention-based graph convolution networks-Reference-Cited by-同舟云学术

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Improved fault detection and diagnosis using graph auto encoder and attention-based graph convolution networks

Published:2024-06 Issue: Volume:11 Page:100158
ISSN:2772-5081
Container-title:Digital Chemical Engineering
language:en
Short-container-title:Digital Chemical Engineering

Author:

Brahmbhatt Parth^ORCID,Patel Rahul^ORCID,Maheshwari Abhilasha,Gudi Ravindra D.

Publisher

Elsevier BV

Reference57 articles.

1. A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems;Md Nor;Reviews in Chemical Engineering,2020

2. Improved Fault Diagnosis Using Dynamic Kernel Scatter-Difference-Based Discriminant Analysis;Sumana;Ind Eng Chem Res,2010

3. Fault Detection and Isolation Using Correspondence Analysis;Detroja;Ind Eng Chem Res,2006

4. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process;Yin;J Process Control,2012

5. Review of Machine Learning Approaches in Fault Diagnosis applied to IoT Systems;Lo,2019

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