Fault Diagnostic Method Based on Deep Learning and Multimodel Feature Fusion for Complex Industrial Processes
Author:
Affiliation:
1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P.R. China
Funder
Ministry of Education of the People's Republic of China
National Natural Science Foundation of China
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.0c03082
Reference38 articles.
1. Diagnosis of multiple and unknown faults using the causal map and multivariate statistics
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5. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
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