Identifying the out-of-control variables of multivariate control chart using ensemble SVM classifiers

Out-of-control signals in multivariate charts may be caused by one or few variables or a set of variables. Multivariate process control often encounters with the diagnosis or interpretation difficulty of an out-of-control signal to determine which variable is responsible for the signal. In this article, we formulate the diagnosis of out-of-control signal as a classification problem. The proposed system includes a shift detector and a classifier. The traditional multivariate chart works as a mean shift detector. Once an out-of-control signal is generated, an SVM-based ensemble classifier is used to recognize the variables that have shifted. We propose using subgroup data and extracted features (sample mean and Mahalanobis distance) as the input vectors of classifier. The performance of the proposed system was evaluated by computing its classification accuracy. We use the traditional decomposition method as a benchmark for comparison. The simulation studies indicate that the proposed ensemble classification model is a successful method in identifying the source of the mean change. The results also reveal that SVM using extracted features as input vector has slightly better classification performance than using raw data as input. The proposed method may facilitate the diagnosis of the out-of-control signal.