Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis (original) (raw)
Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13)
Authors
Messaoud Ramdani, Khaled Mendaci
Available Online August 2013.
DOI
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.
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13) PB - Atlantis Press SP - 792 EP - 797 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2013.112 DO - 10.2991/eusflat.2013.112 ID - Ramdani2013/08 ER -