A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 33 (2022), No. 11, pp. 6158 - 6172
2022article/chapter in journalClosed access
Electrical Engineering and Information TechnologyFaculty of Engineering » Engineering and Information Technology » Automatic Control and Complex Systems
Title in English:
A Comparative Study of Deep Neural Network-Aided Canonical Correlation Analysis-Based Process Monitoring and Fault Detection Methods
Author:
Chen, ZhiwenUDE
- LSF ID
- 53691
- ORCID
-
0000-0002-4759-0904
- Other
- connected with university
corresponding author
- GND
- 134302427
- LSF ID
- 2347
- ORCID
-
0000-0002-5149-5918
- Other
- connected with university
- ORCID
-
0000-0001-7648-6738
- ORCID
-
0000-0002-7662-0471
- ORCID
-
0000-0002-9072-7179
Year of publication:
2022
Open Access?:
Closed access
IEEE ID
Web of Science ID
PubMed ID
Scopus ID
Language of text:
English
Keyword, Topic:
Canonical correlation analysis (CCA) ; Computational modeling ; Correlation ; deep neural network (DNN) ; dynamic process monitoring ; fault detection ; Fault detection ; Nonlinear dynamical systems ; nonlinear process monitoring ; Process monitoring ; State-space methods ; Task analysis