A New Method of On-Line Fault Monitoring Based on Nonlinear MPCA

Multiway principal components analysis (MPCA) is a linear model in nature, thus, limited when it is applied to batch process. In this paper, the linear model MPCA was complemented with an autoassociative neural network model in order to generate nonlinear principal components. The networks bottleneck layer outputs (nonlinear principal components) were made orthogonal. A method to estimate confidence limits based on a kernel probability density function was proposed since the nonlinear scores are no normally distributed. A statistic-like parameter () was proposed to evaluate on-line scores for new runs using the density estimated confidence bounds and replacing the statistic. The proposed method was applied to monitoring fed-batch streptomycete production, and the simulation results show that the nonlinear scores obtained with the autoassociative neural networks capture more process data variance than if obtained with a linear method and the density estimation method proved to be more reliable.