Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model
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 no...
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| Published in | Transactions of Tianjin University Vol. 19; no. 2; pp. 147 - 156 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Tianjin University
01.04.2013
School of Management and Economics, Tianjin University, Tianjin 300072, China |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-4982 1995-8196 |
| DOI | 10.1007/s12209-013-1892-0 |
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| 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. |
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| AbstractList | 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. 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. |
| Author | 张晓蕾 何桢 |
| AuthorAffiliation | School of Management and Economics, Tianjin University, Tianjin 300072, China |
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| Cites_doi | 10.1080/00224065.1994.11979508 10.2307/1270035 10.1002/qre.831 10.1080/07408170490507828 10.1080/00224065.1997.11979767 10.1080/00224065.2002.11980171 10.2307/1270028 10.1080/00224065.1999.11979926 10.1007/b97702 |
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| Copyright | Tianjin University and Springer-Verlag Berlin Heidelberg 2013 Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | time series engineering process control STAR model statistical process control autocorrelation |
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| Notes | statistical process control; engineering process control; time series; STAR model; autocorrelation Zhang Xiaolei , He Zhen （School of Management and Economics, Tianjin University, Tianjin 300072, China） 12-1248/T 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. |
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| References | WielS VTuckerWFaltinFAlgorithmic statistical process control: Concepts and an application[J]Technometrics199234328629710.2307/1270035 CuiJ WXieL YLiuX XStudy on a process control approach integrating SPC with EPC[J]Journal of Northeastern University (Natural Science)200728913171320 JiangWA joint monitoring scheme for automatically controlled processes[J]IIE Transactions200436121201121010.1080/07408170490507828 YuJ LZhangZ WTime series prediction and application for integrated SPC/EPC process disturbance[J]Journal of Zhongyuan University of Technology20092041115 BoxG E PLucenoAStatistical Control by Monitoring and Feedback Adjustment[M]1997USAJohn Wiley TsungFShiJ JWuC F JJoint monitoring of PI-Dcontrolled processes[J]Journal of Quality Technology1999313275286 MacGregorJ FOn-line statistical process control[J]Chemical Engineering Progress198884102131 FanJ QYanQ WNonlinear Time Series: Nonparametric and Parametric Methods[M]2003USASpringer MontgomeryD CKeatsJ BRungerG CIntegrating statistical process control and engineering process control[J]Journal of Quality Technology19942627987 SunQ XGaoQ SZhaoJ LEconomic design of integrating SPC with EPC based on quality feature constraints[J]Chinese Journal of Engineering Design2009166411414 MessinaW SStrategies for the Integration of Statistical and Engineering Process Control[D]1992USAArizona State University ZhangX LHeZNieBAn integrated SPC-EPC study for checking assignable causes resulting in sustained shift based on threshold autoregressive model[J]Chinese Journal of Engineering Design2012194255262 JiangWTsuiK LSPC monitoring of MMSE- and PI-controlled processes[J]Journal of Quality Technology2002344384389 BoxG E PJenkinsG MReinselG CTime Series Analysis: Forecasting and Control[M]1994USAPrentice-Hall ChuWSunS DYuX YStudy on the integration of SPC/EPC and its key technologies[J]Journal of Computer Applications2007271228230 ZivotEWangJ HModeling Financial Time Series with SPLUS[ M]2006USASpringer NembhardH BChenS HCuscore control charts for generalized feedback-control systems[J]Quality and Reli ability Engineering International200723448350210.1002/qre.831 BoxGKramerTStatistical process monitoring and feedback adjustment: A discussion[J]Technometrics1992343251267118320910.2307/1270028 ZhangX LHeZNieBAn integrated SPC-EPC study for checking assignable causes resulting in trend based on TAR model[J]Journal of Management Science20122522432 TsungFApleyD WThe dynamic T2 chart for monitoring feedback-controlled processes[J]IIE Transactions2002341210431053 del CastilloEnriqueStatistical Process Adjustment for Quality Control[M]2002USAJohn Wiley MontgomeryD CStatistical Quality Control: A Modern Introduction[M]2009USAJohn Wiley YuLSunX JLiuFQuality loss analysis for autocorrelation process based on feedback adjustment[J]Control Engineering of China2008153273275 BoxGLucenoADiscrete proportional-integral adjustment and statistical process control[J]Journal of Quality Technology1997293248261 ShiR ZLiuFIntegrated process control approach in 2ndorder dynamic processes[J]Computer Engineering and Applications2010462227229 LiuFShiR ZAnalysis and design of low order EPC controller in statistical process monitoring[J]Control Engineering of China20101712427 R Z Shi (1892_CR21) 2010; 46 G Box (1892_CR5) 1992; 34 W Jiang (1892_CR9) 2002; 34 H B Nembhard (1892_CR11) 2007; 23 F Tsung (1892_CR14) 2002; 34 J Q Fan (1892_CR22) 2003 J W Cui (1892_CR16) 2007; 28 J F MacGregor (1892_CR3) 1988; 84 G E P Box (1892_CR24) 1994 S V Wiel (1892_CR4) 1992; 34 J L Yu (1892_CR19) 2009; 20 D C Montgomery (1892_CR1) 2009 G E P Box (1892_CR8) 1997 L Yu (1892_CR17) 2008; 15 W S Messina (1892_CR6) 1992 G Box (1892_CR12) 1997; 29 X L Zhang (1892_CR26) 2012; 19 W Chu (1892_CR15) 2007; 27 D C Montgomery (1892_CR7) 1994; 26 Enrique Castillo del (1892_CR2) 2002 E Zivot (1892_CR23) 2006 F Tsung (1892_CR13) 1999; 31 Q X Sun (1892_CR18) 2009; 16 X L Zhang (1892_CR25) 2012; 25 F Liu (1892_CR20) 2010; 17 W Jiang (1892_CR10) 2004; 36 |
| References_xml | – reference: ZivotEWangJ HModeling Financial Time Series with SPLUS[ M]2006USASpringer – reference: YuJ LZhangZ WTime series prediction and application for integrated SPC/EPC process disturbance[J]Journal of Zhongyuan University of Technology20092041115 – reference: JiangWTsuiK LSPC monitoring of MMSE- and PI-controlled processes[J]Journal of Quality Technology2002344384389 – reference: JiangWA joint monitoring scheme for automatically controlled processes[J]IIE Transactions200436121201121010.1080/07408170490507828 – reference: BoxGLucenoADiscrete proportional-integral adjustment and statistical process control[J]Journal of Quality Technology1997293248261 – reference: WielS VTuckerWFaltinFAlgorithmic statistical process control: Concepts and an application[J]Technometrics199234328629710.2307/1270035 – reference: MacGregorJ FOn-line statistical process control[J]Chemical Engineering Progress198884102131 – reference: TsungFShiJ JWuC F JJoint monitoring of PI-Dcontrolled processes[J]Journal of Quality Technology1999313275286 – reference: ZhangX LHeZNieBAn integrated SPC-EPC study for checking assignable causes resulting in sustained shift based on threshold autoregressive model[J]Chinese Journal of Engineering Design2012194255262 – reference: MessinaW SStrategies for the Integration of Statistical and Engineering Process Control[D]1992USAArizona State University – reference: NembhardH BChenS HCuscore control charts for generalized feedback-control systems[J]Quality and Reli ability Engineering International200723448350210.1002/qre.831 – reference: TsungFApleyD WThe dynamic T2 chart for monitoring feedback-controlled processes[J]IIE Transactions2002341210431053 – reference: FanJ QYanQ WNonlinear Time Series: Nonparametric and Parametric Methods[M]2003USASpringer – reference: MontgomeryD CKeatsJ BRungerG CIntegrating statistical process control and engineering process control[J]Journal of Quality Technology19942627987 – reference: MontgomeryD CStatistical Quality Control: A Modern Introduction[M]2009USAJohn Wiley – reference: CuiJ WXieL YLiuX XStudy on a process control approach integrating SPC with EPC[J]Journal of Northeastern University (Natural Science)200728913171320 – reference: ZhangX LHeZNieBAn integrated SPC-EPC study for checking assignable causes resulting in trend based on TAR model[J]Journal of Management Science20122522432 – reference: LiuFShiR ZAnalysis and design of low order EPC controller in statistical process monitoring[J]Control Engineering of China20101712427 – reference: del CastilloEnriqueStatistical Process Adjustment for Quality Control[M]2002USAJohn Wiley – reference: YuLSunX JLiuFQuality loss analysis for autocorrelation process based on feedback adjustment[J]Control Engineering of China2008153273275 – reference: SunQ XGaoQ SZhaoJ LEconomic design of integrating SPC with EPC based on quality feature constraints[J]Chinese Journal of Engineering Design2009166411414 – reference: BoxG E PJenkinsG MReinselG CTime Series Analysis: Forecasting and Control[M]1994USAPrentice-Hall – reference: BoxGKramerTStatistical process monitoring and feedback adjustment: A discussion[J]Technometrics1992343251267118320910.2307/1270028 – reference: BoxG E PLucenoAStatistical Control by Monitoring and Feedback Adjustment[M]1997USAJohn Wiley – reference: ChuWSunS DYuX YStudy on the integration of SPC/EPC and its key technologies[J]Journal of Computer Applications2007271228230 – reference: ShiR ZLiuFIntegrated process control approach in 2ndorder dynamic processes[J]Computer Engineering and Applications2010462227229 – volume: 26 start-page: 79 issue: 2 year: 1994 ident: 1892_CR7 publication-title: Journal of Quality Technology doi: 10.1080/00224065.1994.11979508 – volume-title: Statistical Process Adjustment for Quality Control[M] year: 2002 ident: 1892_CR2 – volume-title: Time Series Analysis: Forecasting and Control[M] year: 1994 ident: 1892_CR24 – volume: 20 start-page: 11 issue: 4 year: 2009 ident: 1892_CR19 publication-title: Journal of Zhongyuan University of Technology – volume: 34 start-page: 1043 issue: 12 year: 2002 ident: 1892_CR14 publication-title: IIE Transactions – volume-title: Statistical Quality Control: A Modern Introduction[M] year: 2009 ident: 1892_CR1 – volume: 34 start-page: 286 issue: 3 year: 1992 ident: 1892_CR4 publication-title: Technometrics doi: 10.2307/1270035 – volume: 16 start-page: 411 issue: 6 year: 2009 ident: 1892_CR18 publication-title: Chinese Journal of Engineering Design – volume: 25 start-page: 24 issue: 2 year: 2012 ident: 1892_CR25 publication-title: Journal of Management Science – volume: 23 start-page: 483 issue: 4 year: 2007 ident: 1892_CR11 publication-title: Quality and Reli ability Engineering International doi: 10.1002/qre.831 – volume: 36 start-page: 1201 issue: 12 year: 2004 ident: 1892_CR10 publication-title: IIE Transactions doi: 10.1080/07408170490507828 – volume-title: Strategies for the Integration of Statistical and Engineering Process Control[D] year: 1992 ident: 1892_CR6 – volume: 29 start-page: 248 issue: 3 year: 1997 ident: 1892_CR12 publication-title: Journal of Quality Technology doi: 10.1080/00224065.1997.11979767 – volume-title: Statistical Control by Monitoring and Feedback Adjustment[M] year: 1997 ident: 1892_CR8 – volume: 17 start-page: 24 issue: 1 year: 2010 ident: 1892_CR20 publication-title: Control Engineering of China – volume: 34 start-page: 384 issue: 4 year: 2002 ident: 1892_CR9 publication-title: Journal of Quality Technology doi: 10.1080/00224065.2002.11980171 – volume: 34 start-page: 251 issue: 3 year: 1992 ident: 1892_CR5 publication-title: Technometrics doi: 10.2307/1270028 – volume: 19 start-page: 255 issue: 4 year: 2012 ident: 1892_CR26 publication-title: Chinese Journal of Engineering Design – volume: 31 start-page: 275 issue: 3 year: 1999 ident: 1892_CR13 publication-title: Journal of Quality Technology doi: 10.1080/00224065.1999.11979926 – volume: 28 start-page: 1317 issue: 9 year: 2007 ident: 1892_CR16 publication-title: Journal of Northeastern University (Natural Science) – volume-title: Modeling Financial Time Series with SPLUS[ M] year: 2006 ident: 1892_CR23 – volume: 84 start-page: 21 issue: 10 year: 1988 ident: 1892_CR3 publication-title: Chemical Engineering Progress – volume-title: Nonlinear Time Series: Nonparametric and Parametric Methods[M] year: 2003 ident: 1892_CR22 doi: 10.1007/b97702 – volume: 46 start-page: 227 issue: 2 year: 2010 ident: 1892_CR21 publication-title: Computer Engineering and Applications – volume: 27 start-page: 228 issue: 1 year: 2007 ident: 1892_CR15 publication-title: Journal of Computer Applications – volume: 15 start-page: 273 issue: 3 year: 2008 ident: 1892_CR17 publication-title: Control Engineering of China |
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| Title | Integrated Statistical and Engineering Process Control Based on Smooth Transition Autoregressive Model |
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