Estimation of Nonstationary Process Variance in Multistage Manufacturing Processes Using a Model-Based Observer

Abstract

In this paper, we propose a recursive algorithm to estimate the process variance in multistage manufacturing or assembly processes. We use a replicated model that includes the process variance to be estimated as a time-varying state that changes slowly. For this model, we develop an estimation strategy including tuning parameters that play a direct role in the tradeoff between the estimation accuracy and the adaptation to changes. We also develop a statistical confidence interval for the estimations which enhances the decision of whether the process variances have changed. Unlike other batch methods in the literature, our proposal is computed recursively, and it allows us to tune the tradeoff between the convergence speed and the accuracy without modifying the sample size, which only contains the data of the last manufactured piece.