Comparing Curves Using Additive Models

Walker, Esteban; Wright, S. Paul

doi:10.1080/00224065.2002.11980134

Cited by 144 publications

(100 citation statements)

References 9 publications

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“…Nonlinear profiles monitoring has also been discussed by several authors including Jin and Shi 27 , Walker and Wright 28 Ding et al 29 , Williams et al 30 , Moguerza et al 31 , Vaghefi et al 32 and Jensen and Birch 33 .…”

Section: Introductionmentioning

confidence: 96%

Statistical monitoring of multivariate multiple linear regression profiles in phase I with calibration application

Noorossana

Eyvazian

Amiri

et al. 2009

Quality & Reliability Eng

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In some statistical process control applications, there are some correlated quality characteristics which can be modeled as linear functions of some explanatory variables. We refer to this structure as multivariate multiple linear regression profiles. When the correlation structure between quality characteristics is ignored and profiles are monitored separately then misleading results could be expected. Hence, developing methods to account for this multivariate structure is required. In this paper, we specifically focus on phase I monitoring of multivariate multiple linear regression profiles and develop four methods for this purpose. The performance of the developed methods is compared through simulation studies in terms of probability of a signal. In addition, a diagnostic scheme to find the out-of-control samples is developed. Finally, the application of the proposed methods is illustrated using a calibration application at the National Aeronautics and Space Administration (NASA) Langley Research Center.

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Section: Introductionmentioning

confidence: 96%

Statistical monitoring of multivariate multiple linear regression profiles in phase I with calibration application

Noorossana

Eyvazian

Amiri

et al. 2009

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Soleimani et al (2009) dealt with simple linear profiles with a first-order autoregressive relationship among observations within a profile. Zhu and Lin (2010) proposed a Shewhart control chart for monitoring slopes of linear profiles from the truncated vertical density profiles problem (Walker and Wright, 2002).…”

Section: Methods In Linear 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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“…The principal component analysis (Jones and Rice, 1992) is often used for this purpose. Other work in this category includes Vapnik (1998), Walker and Wright (2002), Woodall et al (2004), and Moguerza et al (2007). Finally, the third category is the use of spatial information.…”

Section: A Solution To Profile Analysis During the Cool-down Stagementioning

confidence: 99%

Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process

Chang

Tsai

Lin

et al. 2012

Quality Engineering

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Comparing Curves Using Additive Models

Cited by 144 publications

References 9 publications

Statistical monitoring of multivariate multiple linear regression profiles in phase I with calibration application

Statistical monitoring of multivariate multiple linear regression profiles in phase I with calibration application

Real-time detection of wave profile changes

Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process

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