Topology-Guided Graph Learning for Process Fault Diagnosis
Author:
Affiliation:
1. Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310023, People’s Republic of China
2. Department of Chemical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan
Funder
National Natural Science Foundation of China
National Science and Technology Council, ROC
Publisher
American Chemical Society (ACS)
Subject
Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.2c03628
Reference38 articles.
1. A review of process fault detection and diagnosis
2. An analysis of process fault diagnosis methods from safety perspectives
3. A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
4. Model-based fault-detection and diagnosis – status and applications
5. Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives
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