Entropy principal component analysis and its application to nonlinear chemical process fault diagnosis-Reference-Cited by-同舟云学术

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Entropy principal component analysis and its application to nonlinear chemical process fault diagnosis

Published:2014-04-02 Issue:5 Volume:9 Page:696-706
ISSN:1932-2135
Container-title:Asia-Pacific Journal of Chemical Engineering
language:en
Short-container-title:Asia-Pac. J. Chem. Eng.

Author:

Deng Xiaogang¹,Tian Xuemin¹

Affiliation:

1. College of Information and Control Engineering; China University of Petroleum (East China); Qingdao Shandong 266580 China

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Waste Management and Disposal,Renewable Energy, Sustainability and the Environment,General Chemical Engineering

Reference35 articles.

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

2. Data-based process monitoring, process control, and quality improvement: Recent developments and applications in steel industry

3. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

4. Nonlinear principal component analysis—Based on principal curves and neural networks

5. Non-linear principal components analysis using genetic programming

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3. Jointly Modeling Drought Characteristics with Smoothed Regionalized SPI Series for a Small Island;Water;2019-11-26

4. Hybrid soft sensor modeling for bisphenol‐A synthesis reaction process;Asia-Pacific Journal of Chemical Engineering;2019-11

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