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

Control charts are commonly used in manufacturing industries for monitoring variations due to assignable causes. 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 of the challenges in multivariate process control is the interpretation of an out-of-control signal. That is, we have to determine which variable is responsible for the signal. 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 multivariate chart works as a mean shift detector. Once an out-of-control signal is generated, an SVM-based classifier will determine which variable is responsible for the mean shift. We propose using subgroup data and extracted features (sample mean and Mahalanobis distance) as the input vectors of classifier. The proposed approach 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. The traditional decomposition method is used as a baseline for comparison. Results from 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.