Fault Detection and Diagnosis of Nonlinear Processes Using Improved Kernel Independent Component Analysis (KICA) and Support Vector Machine (SVM)-Reference-Cited by-同舟云学术

Fault Detection and Diagnosis of Nonlinear Processes Using Improved Kernel Independent Component Analysis (KICA) and Support Vector Machine (SVM)

Published:2008-08-16 Issue:18 Volume:47 Page:6961-6971
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhang Yingwei¹

Affiliation:

1. Key Laboratory of Integrated Automation of Process Industry, Ministry of Education, Northeastern University, Shenyang, Liaoning 110004, Peopleʼs Republic of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ie071496x

Reference29 articles.

1. Statistical process monitoring: basics and beyond

2. Multiple-Fault Diagnosis of the Tennessee Eastman Process Based on System Decomposition and Dynamic PLS

3. Statistical process monitoring with independent component analysis

4. Between-Groups Comparison of Principal Components

5. Fault Detection and Diagnosis in Industrial Systems

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