LAUSR

Traditional control charts assume a baseline parametric model, against which new observations are compared in order to identify significant departures from the baseline model. To monitor a process without a baseline model, real-time contrasts (RTC) control charts were recently proposed to monitor classification errors when seperarting new observations from limited phase I data using a binary classifier. In contrast to the RTC framework, the distance between an in-control dataset and a dataset of new observations can also be used to measure the shift of the process. In this paper, we propose a distance-based multivariate process control chart using support vector machines (SVM), referred to as D-SVM chart. The SVM classifier provides a continuous score or distance from the boundary for each observation and allows smaller sample sizes than the previously random forest based RTC charts. An extensive experimental study shows that the RTC charts with the SVM scores are more efficient than those with the random forest for detecting changes in high-dimensional processes and/or non-normal processes. A real-life example from a mobile phone assembly process is also considered.