Abstract
Although most statistical process control techniques are designed to detect constant process shifts, time-varying shift patterns are more frequently encountered in industrial practice. In this paper, we propose an adaptive T2 scheme for monitoring processes with dynamic shifts. The new scheme preserves the optimality of directionally variant charts by updating a reference mean-shift vector recursively and can be easily adjusted to obtain high sensitivity across desired shift ranges. Simulation studies show that the adaptive T2 chart with EWMA forecasting of mean shifts outperforms most conventional charts in a dynamic environment and is also robust to parameter-estimation uncertainties.
Additional information
Notes on contributors
Kaibo Wang
Dr. Wang is an Assistant Professor in the Department of Industrial Engineering. His email address is [email protected].
Fugee Tsung
Dr. Tsung is an Associate Professor in the Department of Industrial Engineering and Logistics Management. He is a Senior Member of ASQ. His email address is [email protected].