Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis
Messaoud Ramdani, Khaled Mendaci
Available Online August 2013.
- https://doi.org/10.2991/eusflat.2013.112How to use a DOI?
- Process monitoring fuzzy clustering local statistics control confidence limits biological process
- 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.
- 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 -