Exploiting neural PCA and Fisher discriminate analysis for FDI system

Abstract

The major goal of this paper is the description of a fault detection and isolation system. Such a system is geared to the complex processes through the combination of the neural networks, Fisher discriminate analysis and the principal component analysis. The corner stone of this work is the application of a self-associative neural network to the nonlinear PCA for fault detection by starting with a various measurement multivariable matrix stemming from a complex industrial process. FDA and PLS are, then, used to identify the directions of the detected faults through the classification of the groups with and without faults. To isolate these faults is to recognize the variables that are the cause of these faults. This is attained by calculating the contribution; in other words, the variables having the largest contribution with regard to the others are considered defective. This statistical approach is authenticated on a pastry production process and gives good results. By comparing FDI with other methods, we can perceive that our approach gives more reliable results.