A general framework for monitoring complex processes with both in-control and out-of-control information

Processes monitoring using multivariate quality variables remains an important and challenging problem in statistical process control (SPC). Although multivariate SPC has been extensively studied in the literature, the challenges associated with designing robust and flexible control schemes have yet to be adequately addressed. This paper develops a general monitoring framework for detecting location shifts in complex processes by employing data mining methods. The historical in-control (IC) and out-of-control (OC) data are combined to set up a support vector machine (SVM) model. The working status of the process is indicated by the probabilistic outputs of the SVM classifier and the multivariate exponentially weighted moving average strategy is applied to construct the control chart. A fast diagnostic procedure can be implemented as soon as the control chart gives an alarm. Our numerical studies show that the proposed control chart is able to deliver satisfactory IC and OC run-length performance regardless of the underlying distributions and data types. An example using real data from an industrial application demonstrates the effectiveness of the proposed method.