Combined Shewhart-EWMA (CSEWMA) control charts for two-sample high-dimensional data

This paper proposes a novel statistical process control methodology by developing Combined Shewhart-EWMA (CSEWMA) control charts designed for high-dimensional two-sample data. These charts are highly proficient in identifying process variations, meeting the increasing demand for advanced SPC techniques in complex industrial contexts. The suggested methodology includes three unique control charts, DRCSEWMA, BSCSEWMA and SDCSEWMA, which are based on the Dempster (DR), Bai and Saranadasa (BS) and Srivastava–Du (SD) statistics, respectively. These charts are optimized to detect small to moderate process shifts and are evaluated using metrics such as the average and standard deviation of the run length and extra quadratic loss. The effectiveness of the proposed charts is benchmarked against each other and against conventional Shewhart and EWMA-based approaches in high-dimensional scenarios, including multivariate normal, t, and gamma data structures. Practical applicability is demonstrated using wind turbine bearing and daily stock price fluctuation datasets, where real-time responses from high-dimensional data are essential. The DRCSEWMA, BSCSEWMA and SDCSEWMA control charts exhibit exceptional sensitivity and efficient monitoring solutions. By enabling the early detection of process shifts, these charts enhance safety, operational efficiency and decision-making accuracy in environmental engineering processes, highlighting their potential for broader applications in high-dimensional environments.