ABSTRACT
ABSTRACT In this chapter, some Bayesian algorithms for detecting a persistent process mean shift and for adjusting the process back to target are presented. We discuss the connection between the Bayesian algorithm and the cumulative sum (CUSUM) algorithm, a popular tool for detecting small process mean shifts. The process adjustment method is based on a Kalman filter technique, which provides a sequential adjustment strategy along with process measurements. The integration of this sequential adjustment method with different control charts is evaluated through simulations.
