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

被引：0

作者：

张晓蕾 ^{[1
]}

何桢 ^{[1
]}

机构：

[1] School of Management and Economics,Tianjin University

来源：

Transactions of Tianjin University | 2013年 / 02期

基金：

中国国家自然科学基金;

关键词：

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

D O I：

暂无

中图分类号：

O212.1 [一般数理统计];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：147 / 156

页数：10

共 50 条

[1] Integrated statistical and engineering process control based on smooth transition autoregressive model
Zhang X.
He Z.
Transactions of Tianjin University, 2013, 19 (02) : 147 - 156
[2] Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model
张晓蕾
何桢
Transactions of Tianjin University, 2013, 19 (02) : 147 - 156
[3] An integrated model based on statistical process control and maintenance
Mehrafrooz, Zohreh
Noorossana, Rassoul
COMPUTERS & INDUSTRIAL ENGINEERING, 2011, 61 (04) : 1245 - 1255
[4] The fractional integrated bi- parameter smooth transition autoregressive model
El Montasser, Ghassen
Ajmi, Ahdi Noomen
ECONOMICS BULLETIN, 2012, 32 (01): : 755 - 765
[5] An integrated model of statistical process control and maintenance based on the delayed monitoring
Yin, Hui
Zhang, Guojun
Zhu, Haiping
Deng, Yuhao
He, Fei
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 133 : 323 - 333
[6] Forecasting ENSO with a smooth transition autoregressive model
Ubilava, David
Helmers, C. Gustav
ENVIRONMENTAL MODELLING & SOFTWARE, 2013, 40 : 181 - 190
[7] A smooth transition autoregressive conditional duration model
Chiang, Min-Hsien
STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS, 2007, 11 (01):
[8] Integrated statistical process control and engineering process control for a manufacturing process with multiple tools and multiple products
Lee, Shui-Pin
Wong, David Shan-Hill
Sun, Cheng-I
Chen, Wun-Hwa
Jang, Shi-Shang
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING, 2015, 32 (03) : 174 - 185
[9] Wind power forecasting based on outlier smooth transition autoregressive GARCH model
Hao CHEN
Fangxing LI
Yurong WANG
JournalofModernPowerSystemsandCleanEnergy, 2018, 6 (03) : 532 - 539
[10] Wind power forecasting based on outlier smooth transition autoregressive GARCH model
Chen, Hao
Li, Fangxing
Wang, Yurong
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2018, 6 (03) : 532 - 539

← 1 2 3 4 5 →