Interpreting the Mean Shift Signals in Multivariate Control Charts Using Support Vector Machine-Based Classifier

As one of the primary Statistical Process Control (SPC) tools, control chart plays a very important role in attaining process stability. There are many cases in which the simultaneous monitoring or control of two or more related quality characteristics is required. Out-of-control signals in multivariate charts may be caused by one or more variables or a set of variables. One difficulty encountered with any multivariate process control is the diagnosis or interpretation of an out-of-control signal to determine which variable is responsible for the signal. In this paper, the diagnosis of out-of-control signal is formulated 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 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 proposed classifier will be demonstrated by multivariate processes with two and three quality characteristics. The performance of the proposed system was evaluated by computing its classification accuracy. We use the traditional decomposition method as a benchmark for comparison. Simulation studies indicate that the proposed approach is a successful method in identifying the source of mean change. The results 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.