Nonparametric Profile Monitoring by Mixed Effects Modeling

Qiu, Peihua; Zou, Changliang; Wang, Zhaojun

doi:10.1198/tech.2010.08188

Cited by 207 publications

(154 citation statements)

References 33 publications

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“…To monitor the nonlinear profiles, Woodall (2007) categorized approaches into four types: (1) applying multiple and polynomial regression (Zou et al, 2007;Kazemzadeh et al 2008;Mahmoud 2008); (2) applying nonlinear regression models (Ding et al, 2006;Williams et al, 2007;Shiau et al, 2009;Chang and Yadama 2010;Chen and Nembhard 2011); (3) use of mixed models (Jensen et al, 2008;Jensen and Birch, 2009;Qiu et al, 2010;Abdel-Salam, et al, 2013); and (4) use of wavelets (Reis and Saraiva, 2006;Zhou et al, 2006;Chicken et al, 2009). In this section, we will update recent developed methods according to these categories.…”

Section: Methods In Nonlinear Profile Monitoringmentioning

confidence: 99%

Real-time detection of wave profile changes

Chang

Tavakkol

Chou

et al. 2014

Computers & Industrial Engineering

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SA statistical process control (SPC) framework is proposed to detect potential changes of a wave profile on a real-time basis. In regular profile monitoring, change detection takes place when a complete profile is generated. In this study, the detection of a potential profile change takes place before the entire information on the profile of interest is fully available. The main research goal is to make a correct process decision as soon as possible. A real-world example of condensation-water-temperature profile monitoring was used to demonstrate the proposed framework. A simulation study was also conducted. The simulation results confirm that the proposed framework is capable of detecting profile changes without having to wait for the entire profile to be generated.

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Section: Methods In Nonlinear Profile Monitoringmentioning

confidence: 99%

Real-time detection of wave profile changes

Chang

Tavakkol

Chou

et al. 2014

Computers & Industrial Engineering

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“…Therefore, in the absence of strict distributional assumptions, it is always better to use the nonparametric control charts. We may nd a variety of literature including some recent contributions in the direction of nonparametric control charts such as [4][5][6][7][8][9][10][11][12][13][14], among many others.…”

Section: Introductionmentioning

confidence: 99%

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

Ahmad

Riaz

2017

Scientia Iranica

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Abstract. Control charts are popular tounderols for process monitoring and are mainly classi ed into memory and memory-less structures. Exponentially Weighted Moving Average control charts (EWMA) are widely used for the detection of small shifts in process parameters. The traditional design structures of these types of charts rely on the stringent assumption of normality. In practice, quality characteristics generally do not follow normality; hence, alternative charting structures are needed. Nonparametric control chart is one such alternative that do not depend on the mentioned distributional assumption(s). This study deals with nonparametric EWMA charts for an e cient detection of smaller shifts in location. We have investigated asymptotic, time-varying, and Fast Initial Response (FIR) based time varying control limits for nonparametric EWMA charts. We have considered two variants of FIR with time varying limits for nonparametric EWMA structure. A variety of run length properties have been investigated for performance evaluations. It is observed that FIR-based nonparametric EWMA charts with time-varying limits are quite sensitive to detecting smaller shifts, and they o er attractive run length properties. A real-life example is also provided to illustrate the application of the time-varying and FIR-based nonparametric EWMA control charts.

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“…Kim et al (2003) showed their method of three univariate EWMA charts monitoring slope, intercept, and the variance of deviation between samples and regression line performs better than EWMA/R chart in terms of average run length (ARL). Studies in nonlinear profile analysis can be categorized into four typesapplying multiple and polynomial regression (Zou et al, 2007;Kazemzadeh et al 2008;Mahmoud 2008), applying nonlinear regression models (Ding et al, 2006;Williams et al, 2007;Shiau et al, 2009;Chang and Yadama 2010;Chen and Nembhard 2011;), use of mixed models (Jensen et al, 2008;Jensen and Birch, 2009;Qiu et al, 2010;Paynabar and Jin, 2011 ), and use of wavelets (Reis and Saraiva, 2006;Zhou et al, 2007;Chicken et al, 2009). For more detail of those methods to monitor the process stability can be found in Woodall (2007) and Noorossana et al (2011).…”

Section: Introductionmentioning

confidence: 99%

On monitoring of multiple non-linear profiles

Chou

Chang

Tsai

2013

International Journal of Production Research

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Most state-of-the-art profile monitoring methods involve studies of one profile. However, a process may contain several sensors or probes that generate multiple profiles over time. Quality characteristics presented in multiple profiles may be related multiple aspects of product or process quality. Existing charting methods for simultaneous monitoring of each multiple profile may result in higher false alarm rates. Or worse, they cannot correctly detect potential profile relationship changes. In this study, we propose two approaches to detect process shifts in multiple nonlinear profiles. A simulation study was conducted to evaluate the performance of the proposed approaches in terms of average run length under different process shift scenarios. Pros and cons of the proposed methods are discussed. A guideline for choosing the proposed methods is introduced. In addition, a hybrid method combining the salient points of both approaches is explored. Finally, a real-world data set from a vulcanization process is used to demonstrate the implementation of the proposed methods.

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Nonparametric Profile Monitoring by Mixed Effects Modeling

Cited by 207 publications

References 33 publications

Real-time detection of wave profile changes

Real-time detection of wave profile changes

On enhanced sensitivity of nonparametric EWMA control charts for process monitoring

On monitoring of multiple non-linear profiles

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