Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model

• Published in: Transactions of Tianjin University Vol. 19; no. 2; pp. 147 - 156
• Main Author: 张晓蕾 何桢
• Format: Journal Article
• Language: English
• Published: Heidelberg Tianjin University 01.04.2013; School of Management and Economics, Tianjin University, Tianjin 300072, China
• Subjects: Engineering; Humanities and Social Sciences; Mechanical Engineering; multidisciplinary; Science; 基础; 工程过程控制; 平滑过渡; 时间序列模型; 综合控制系统; 综合统计; 自回归模型; 非线性系统; time series; engineering process control; STAR model; statistical process control; autocorrelation
• ISSN: 1006-4982; 1995-8196
• DOI: 10.1007/s12209-013-1892-0

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.