Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes

Non-random (abnormal) behaviour indicates that a process is under the influence of special causes of variation. Detection of abnormal patterns is well established in univariate statistical process control (SPC). Various solutions including heuristics, traditional computer programming, expert systems and neural networks (NNs) have been successfully implemented. In multivariate SPC (MSPC), on the other hand, there is a clear need for more investigations into pattern detection. Bivariate SPC is a special case of MSPC where the number of variates is two and is studied here in terms of identification of shift patterns. In this work, an existing NN classification technique—known as novelty detection (ND)—whose application for MSPC has not been reported is applied for pattern recognition. ND successfully detects non-random bivariate time-series patterns representing shifts of various magnitudes in the process mean vector. The investigation proposes a simple heuristic approach for applying ND as an effective and useful tool for pattern detection in bivariate SPC with potential applicability for MSPC in general.