No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models-Reference-Cited by-同舟云学术

No-Delay Multimodal Process Monitoring Using Kullback-Leibler Divergence-Based Statistics in Probabilistic Mixture Models

Published:2023-01 Issue:1 Volume:20 Page:167-178
ISSN:1545-5955
Container-title:IEEE Transactions on Automation Science and Engineering
language:
Short-container-title:IEEE Trans. Automat. Sci. Eng.

Author:

Cao Yue¹^ORCID,Jan Nabil Magbool²^ORCID,Huang Biao³^ORCID,Wang Yalin⁴^ORCID,Pan Zhuofu⁴,Gui Weihua⁴^ORCID

Affiliation:

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

2. Department of Chemical Engineering, Indian Institute of Technology Tirupati, Tirupati, India

3. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada

4. School of Automation, Central South University, Changsha, China

Funder

Major Program of National Natural Science Foundation of China

“Overseas Expertise Introduction Project for Discipline Innovation” 111 Project

Projects of International Cooperation and Exchanges NSFC

Program of China Scholarship Council

Fundamental Research Funds for Central Universities of Central South University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8856/10007430/09695517.pdf?arnumber=9695517

Reference42 articles.

1. Multimode Process Monitoring Using Variational Bayesian Inference and Canonical Correlation Analysis

2. Hierarchical hybrid distributed PCA for plant-wide monitoring of chemical processes

3. A comprehensive hybrid first principles/machine learning modeling framework for complex industrial processes

4. Hierarchically Distributed Monitoring for the Early Prediction of Gas Flare Events

5. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models

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