Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines

Process control charts are important tools for monitoring process variation in manufacturing industries. There are many situations in which the simultaneous monitoring or control of two or more related quality characteristics is necessary. Out-of-control signals in multivariate charts may be caused by one or more variables or a combination of variables. One difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine which variable is responsible for the signal. A novel approach for identifying the source of variance shifts in the multivariate process is presented in this paper. In this study, we formulated the interpretation of out-of-control signal as a classification problem. The proposed system includes a shift detector and a classifier. The traditional generalized variance chart works as a variance shift detector. When an out-of-control signal is generated, a classifier will determine which variable is responsible for the variance shift. We consider two classifiers based on neural networks (NN) and support vector machines (SVM). We propose using subgroup data and some extracted features as predictors. The performance of the proposed system was evaluated by computing its classification accuracy. Results from simulation studies indicate that the proposed approach is a successful method in identifying the source of variance change. The results indicate that the NN-based classifier and SVM classifier have similar classification performance. An illustrative example is given to describe the applications of the proposed methods in manufacturing process control. The proposed method may facilitate the diagnosis of the out-of-control signal.