Robust monitoring of industrial processes using process data with outliers and missing values-Reference-Cited by-同舟云学术

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Robust monitoring of industrial processes using process data with outliers and missing values

Published:2019-09 Issue: Volume:192 Page:103827
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Luo Lijia,Bao Shiyi,Peng Xin

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference36 articles.

1. Fault detection and diagnosis based on sparse PCA and two-level contribution plots;Luo;Ind. Eng. Chem. Res.,2017

2. Survey on data-driven industrial process monitoring and diagnosis;Qin;Annu. Rev. Contr.,2012

3. Monitoring, fault diagnosis, fault tolerant control and optimization: data driven methods;MacGregor;Comput. Chem. Eng.,2012

4. Review on data-driven modeling and monitoring for plant-wide industrial processes;Ge;Chemometr. Intell. Lab. Syst.,2017

5. Process data analytics via probabilistic latent variable models: a tutorial review;Ge;Ind. Eng. Chem. Res.,2018

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