Process monitoring based on classification tree and discriminant analysis

Abstract

To cope with the computational intensity associated with classification tree analysis and the multicolinearity in the process data, a newly developed process monitoring scheme integrating classification tree and Fisher Discriminant Analysis (FDA) is developed. FDA extracts the most significant components in the original process data and achieves optimal discriminating among different faults. Classification tree uses the FDA scores, which are the lower dimensional representation produced by FDA, to separate observations into different fault classes. A stopping rule is applied to determine the optimal order of FDA. Two case studies are presented to illustrate the effectiveness of the proposed methods compared with the original classification tree. The new method generates better classification accuracy and uses less construction time.