Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)

Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis

Authors
Messaoud Ramdani, Khaled Mendaci
Corresponding Author
Messaoud Ramdani
Available Online August 2013.
DOI
https://doi.org/10.2991/eusflat.2013.112How to use a DOI?
Keywords
Process monitoring fuzzy clustering local statistics control confidence limits biological process
Abstract
Traditional process monitoring techniques assume the normal operating conditions (NOC) to be distributed normally. However, for processes with more than one operating regime, building a single subspace model to monitor the whole process operation performance may not be efficient and will lead to high rate of missing alarm. To handle this situation, a monitoring strategy using multiple subspace models is presented in this paper based on fuzzy clustering. From the experimental results using a simultion model of a continous ow aerated bioreactor for wastewater treatment in pulp and paper industry it has been shown that the proposed approach is very promising.
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Proceedings
8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
978-90786-77-78-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/eusflat.2013.112How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Messaoud Ramdani
AU  - Khaled Mendaci
PY  - 2013/08
DA  - 2013/08
TI  - Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis
BT  - 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2013.112
DO  - https://doi.org/10.2991/eusflat.2013.112
ID  - Ramdani2013/08
ER  -