A novel hybrid ICA-SVM method for detection and identification of shift in multivariate processes

Abstract

Detecting shifts in the mean vector of a multivariate statistical process control is crucial, and equally important is identifying the source of such a signal. This study introduces a novel approach that combines independent components analysis with support vector machines to address the challenge of multivariate process monitoring. In this hybrid independent components analysis-support vector machines method, statistical metrics $I^2$ derived from the independent components extracted through independent components analysis from observed data serve as input variables for the support vector machines. The probabilistic outputs generated by the support vector machines model are utilized as monitoring statistics for the proposed control chart, referred to as $I^2-\text{PoC}$. Simulation results validate the effectiveness of the independent components analysis with support vector machines approach in both detecting and identifying shifts in multivariate control processes, whether they follow a normal or non-normal distribution. Furthermore, the results demonstrate the robustness of this method in handling various challenges, including complex relationships between process variables, shifts of varying sizes, and different distribution shapes, when compared to existing approaches in the literature.