SPC Monitoring and Variance Estimation

Abstract

According to W. Shewhart, process variation can be classified into assignable cause and common cause variations. Assignable cause variation can be eliminated by statistical process control (SPC) methods through identification and elimination of the root cause of the process shift. Common cause variation is inherent in the process and is generally difficult to reduce by SPC methods. However, if the common cause variation can be modeled by an autocorrelated process and physical variables are available to adjust the output, the common cause variation can be reduced by automatic process control (APC) methods through feedback/feedforward controllers. Integration of SPC and APC methods can result in major improvements in industrial efficiency. Most SPC monitoring methods and traditional APC process adjustment methods (such as those based on one-step-ahead minimum mean squared error predictors or proportional integral derivative controllers) involve one or both of the following two steps: (i) “whitening” the process by subtracting a predictor and (ii) monitoring the prediction errors with appropriate control limits. This paper reviews common process monitoring and adjustment methods for process control of autocorrelated data, including model-free methods based on batch means, and investigates the general relationships and properties of the underlying models. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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