Extracting Dissimilarity of Slow Feature Analysis between Normal and Different Faults for Monitoring Process Status and Fault Diagnosis-Reference-Cited by-同舟云学术

Extracting Dissimilarity of Slow Feature Analysis between Normal and Different Faults for Monitoring Process Status and Fault Diagnosis

Published:2019-03-20 Issue:3 Volume:52 Page:283-292
ISSN:0021-9592
Container-title:JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
language:en
Short-container-title:J. Chem. Eng. Japan / JCEJ

Author:

Zheng Haiyong¹,Yan Xuefeng¹

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology

Publisher

Society of Chemical Engineers, Japan

Subject

General Chemical Engineering,General Chemistry

Link

https://www.jstage.jst.go.jp/article/jcej/52/3/52_18we079/_pdf

Reference31 articles.

1. Alabi, S. I., A. J. Morris and E. B. Martin; “On-Line Dynamic Process Monitoring Using Wavelet-Based Generic Dissimilarity Measure,” Chem. Eng. Res. Des., 83, 698–705 (2005)

2. Blaschke, T., P. Berkes and L. Wiskott; “What is the Relation between Slow Feature Analysis and Independent Component Analysis?” Neural Comput., 18, 2495–2508 (2006)

3. Bohmer, W., S. Griinewalder and H. Nickisch; “Generating Feature Spaces for Linear Algorithms with Regularized Sparse Kernel Slow Feature Analysis,” Mach. Learn., 89, 67–86 (2012)

4. Choi, S. W. and I. B. Lee; “Nonlinear Dynamic Process Monitoring Based on Dynamic Kernel PCA,” Chem. Eng. Sci., 59, 5897–5908 (2004)

5. Downs, J. J. and E. F. Vogel; “A Plant-Wide Industrial Process Control Problem,” Comput. Chem. Eng., 17, 245–255 (1993)

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An industrial process fault diagnosis method based on independent slow feature analysis and stacked sparse autoencoder network;Journal of the Franklin Institute;2024-01

2. Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis;ACS Omega;2022-02-16

3. A Novel Process Monitoring Method Based on Dynamic Related ReliefF-SFA Method;IEEE Access;2020

4. Generalized grouped contributions for hierarchical fault diagnosis with group Lasso;Control Engineering Practice;2019-12