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
Reference39 articles.
1. Abnormal situation management: Challenges and opportunities in the big data era
2. An analysis of process fault diagnosis methods from safety perspectives
3. Methods and models in process safety and risk management: Past, present and future
4. Abnormal situation management for smart chemical process operation
5. Safety and risk analysis in digitalized process operations warning of possible deviating conditions in the process environment
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