Nonlinear Statistical Process Monitoring based on Competitive Principal Component Analysis

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. From the experimental results using a simulation model of a continuous flow aerated bioreactor for wastewater treatment in pulp and paper industry it has been shown that the proposed approach is very promising.