Nonlinear dynamic process monitoring using deep dynamic principal component analysis
Author:
Affiliation:
1. College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, People's Republic of China
2. Institute of Zhejiang University-Quzhou, Quzhou, People's Republic of China
Funder
Institute of Zhejiang University-Quzhou Science and Technology Project
Publisher
Informa UK Limited
Subject
Artificial Intelligence,Control and Optimization,Control and Systems Engineering
Link
https://www.tandfonline.com/doi/pdf/10.1080/21642583.2021.2024915
Reference22 articles.
1. Braatz, R. (2009). Tennessee eastman problem simulation data. Retrieved from http://web.mit.edu/braatzgroup/links.html.
2. Bayesian Networks in Fault Diagnosis
3. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis
4. Deep Principal Component Analysis Based on Layerwise Feature Extraction and Its Application to Nonlinear Process Monitoring
5. Guest Editorial: Special section on data-driven approaches for complex industrial systems
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