New Dynamic Predictive Monitoring Schemes Based on Dynamic Latent Variable Models-Reference-Cited by-同舟云学术

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New Dynamic Predictive Monitoring Schemes Based on Dynamic Latent Variable Models

Published:2020-01-10 Issue:6 Volume:59 Page:2353-2365
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Dong Yining¹²,Qin S. Joe³^ORCID

Affiliation:

1. Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States

2. The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, Shenzhen, Guangdong 518172, China

3. School of Data Science, City University of Hong Kong, Kowloon, Hong Kong

Funder

Shenzhen Science and Technology Innovation Commission

Chinese University of Hong Kong

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.9b04741

Reference41 articles.

1. Statistical process control of multivariate processes

2. Survey on data-driven industrial process monitoring and diagnosis

3. Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application

4. Review and Perspectives of Data-Driven Distributed Monitoring for Industrial Plant-Wide Processes

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1. Semi-Supervised Dynamic Latent Variable Regression for Prediction and Quality-Relevant Fault Monitoring;IEEE Transactions on Control Systems Technology;2024-07

2. Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning;International Journal of Hydrogen Energy;2024-06

3. On the use of DPCA in process fault detection and identification;2024 International Conference on Control, Automation and Diagnosis (ICCAD);2024-05-15

4. Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis;Journal of Process Control;2024-03

5. Graph attention network with Granger causality map for fault detection and root cause diagnosis;Computers & Chemical Engineering;2024-01

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