Deep neural network based recursive feature learning for nonlinear dynamic process monitoring-Reference-Cited by-同舟云学术

Deep neural network based recursive feature learning for nonlinear dynamic process monitoring

Published:2019-12-12 Issue:4 Volume:98 Page:919-933
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can J Chem Eng

Author:

Zhu Jiazhen¹,Shi Hongbo¹,Song Bing¹,Tan Shuai¹,Tao Yang¹

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of EducationEast China University of Science and Technology Shanghai P. R. China

Funder

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.23669

Reference43 articles.

1. Fault Detection and Classification Using Quality-Supervised Double-Layer Method

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3. Slow-Feature-Analysis-Based Batch Process Monitoring With Comprehensive Interpretation of Operation Condition Deviation and Dynamic Anomaly

4. Review of Recent Research on Data-Based Process Monitoring

5. A Bibliometric Review and Analysis of Data-Driven Fault Detection and Diagnosis Methods for Process Systems

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