Real-Time Monitoring of the Quality of Multivariate Processes with a SVM Based Classifier Ensemble Approach

Abstract

Using data acquisition systems and computers in on-line process control has led to increased interest in multivariate statistical process control (SPC) in which several interrelated quality variables are simultaneously monitored. Learning based techniques, especially neural networks, have been applied to detect mean shifts in multivariate processes with promising results. However, neural networks suffer from generalization problems due to overfitting. Support vector machines (SVMs) avoid the overfitting problem by adopting the structure risk minimization principle in the learning process. Classifier ensembles (i.e., combining of multiple classifiers) have been proven to be a method superior to single classifiers in many complex pattern recognition problems. With the SVM based classifier ensemble technique, this study proposes a straightforward and effective model to on-line recognize mean shifts in multivariate processes. Empirical results using simulation show that the proposed classifier ensemble model can not only efficiently recognize the mean shifts but also accurately identify the variables that have deviated from their original means. The shift direction of each of the deviated variables can also be simultaneously determined. Numerical comparisons in a bivariate scenario indicate that the proposed SVM based classifier ensemble model outperforms neural network models, SVM models, and conventional multivariate SPC approaches reported in the literature in terms of average run length. This study is useful for quality practitioners who seek efficient methods for on-line recognizing mean shifts in multivariate processes, where the investigation resulting from a false recognition is costly.