Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network
Author:
Affiliation:
1. School of Automation, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2. Guangzhou Intelligent Building Equipment Information Integration and Control Key Laboratory, Guangzhou 510665, China
Funder
tthe Guangdong Special Project in Key Field of Artificial Intelligence for the Ordinary University
the Guangzhou Key Laboratory Construction Project
the Innovative Team Project of the Ordinary University of Guangdong Province
the Guangzhou Key Research and Development Project
Publisher
American Chemical Society (ACS)
Subject
General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c04017
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