Output details
12 - Aeronautical, Mechanical, Chemical and Manufacturing Engineering
Queen's University Belfast
Article title
Improved Process Monitoring Using Nonlinear Principal Component Models
Type
D - Journal article
Title of journal
International Journal of Intelligent Systems
Article number
-
Volume number
23
Issue number
5
First page of article
520
ISSN of journal
0884-8173
Year of publication
2008
URL
-
Number of additional authors
3
Additional information
This paper presents two new approaches for use in process monitoring. The first is a combined principal component analysis and neural network technique that is more computationally efficient than traditional techniques. The second is a data reconstruction technique that can identify erroneous data from a faulty sensor and replace it with new data from remaining sensors. The research was supported by an EPSRC grant (EP/C005457/1) in conjunction with Electrical Engineering. The techniques presented in this paper facilitated a new research area for the School in area of fault identification within internal combustion engines.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
