Variable selection-based SPC procedures for high-dimensional multistage processes

Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring (SPC) approaches for monitoring and controlling quality in high-dimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average (MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length (ARL) for the detection of process mean shift.