Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis

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/).

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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  -