Integrated statistical and engineering process control based on smooth transition autoregressive model

Abstract

Traditional studies on integrated statistical process control and engineering process control (SPC-EPC) are based on linear autoregressive integrated moving average (ARIMA) time series models to describe the dynamic noise of the system. However, linear models sometimes are unable to model complex nonlinear autocorrelation. To solve this problem, this paper presents an integrated SPC-EPC method based on smooth transition autoregressive (STAR) time series model, and builds a minimum mean squared error (MMSE) controller as well as an integrated SPC-EPC control system. The performance of this method for checking the trend and sustained shift is analyzed. The simulation results indicate that this integrated SPC-EPC control method based on STAR model is effective in controlling complex nonlinear systems.

Keywords

statistical process control / engineering process control / time series / STAR model / autocorrelation

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