Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression

Shiau, Jyh‐Jen Horng; Huang, Hsin‐Chun; Lin, Shuo-Hui; Tsai, Ming-Ye

doi:10.1080/03610920802702535

Cited by 41 publications

(34 citation statements)

References 12 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%

“…The LMM also allows considering the profiles as a random sample of profiles from a common population distribution, which may be a more realistic assumption than assuming that the profiles are completely independent of each other. Shiau et al (2009) proposed a method for monitoring nonlinear profiles with random effects by nonparametric regression methods. They used the technique of principal components analysis to analyze the covariance structure of the profiles.…”

Section: Methods In Nonlinear Profile Monitoringmentioning

confidence: 99%

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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%

Section: Methods In Nonlinear Profile Monitoringmentioning

confidence: 99%

Real-time detection of wave profile changes

Chang

Tavakkol

Chou

et al. 2014

Computers & Industrial Engineering

View full text Add to dashboard Cite

show abstract

“…Profile monitoring involves fitting parametric or nonparametric models to longitudinal or profile data and monitoring these models. Some applications are discussed in Wang and Tsung [2005], Shiau et al [2009], Jensen et al [2006], and Mosesova et al [2006]. In Wang and Tsung [2005], the authors propose an SPC method to monitor processes with large amounts of data.…”

Section: Previous Workmentioning

confidence: 99%

Scalable Statistical Monitoring of Fleet Data

Chu

Gorinevsky

Boyd

2011

IFAC Proceedings Volumes

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This paper considers the problem of fitting regression models to historical fleet data with mixed effects, which arises in the context of statistical monitoring of data from a fleet (population) of similar units. A fleet-wide extension of the multivariable statistical process control approach is used to monitor for three different types of faults: a performance anomaly, a performance shift, and an anomalous unit. Our formulation requires the solution of a leastsquares problem with very large numbers of both regressors (variables) and data measurements. For problems of interest, this least-squares problem cannot be solved using standard methods. We propose a method for solving the problem that is scalable to extremely large datasets, even ones that do not fit in to the memory of a single computer system. Our method can be parallelized, but also works serially on a single processor. This approach is demonstrated in a simulated example for monitoring a fleet of aircraft from historical cruise flight data.

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“…They proposed an independent component analysis (ICA) method for the dimension reduction and a change pointbased method for the effective separation of in-control and out-of-control profiles. Shiau et al 2009 propose for Phase I profile monitoring, using the usual Hotelling T 2 chart, a run of the mail accustomed to control chart designed for multivariate process data, by treating the principal component (PC) scores of a side-view obtained from PCA as the multivariate data. For Phase II process monitoring, they propose PC-score charts and a T 2 chart (different from the T 2 chart of Phase I).…”

Section: Nonlinear Profilesmentioning

confidence: 99%

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

Zakour¹,

Taleb

2017

J Ind Eng Int

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Endpoint detection is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done in the right way, especially if the etched area is very small (0.1%). It truly is a crucial part of supplying repeatable effects in every single wafer. When the film being etched has been completely cleared, the endpoint is reached. To ensure the desired device performance on the produced integrated circuit, the high optical emission spectroscopy (OES) sensor is employed. The huge number of gathered wavelengths (profiles) is then analyzed and pre-processed using a new proposed simple algorithm named Spectra peak selection (SPS) to select the important wavelengths, then we employ wavelet analysis (WA) to enhance the performance of detection by suppressing noise and redundant information. The selected and treated OES wavelengths are then used in modified multivariate control charts (MEWMA and Hotelling) for three statistics (mean, SD and CV) and windowed polynomial regression for mean. The employ of three aforementioned statistics is motivated by controlling mean shift, variance shift and their ratio (CV) if both mean and SD are not stable. The control charts show their performance in detecting endpoint especially W-mean Hotelling chart and the worst result is given by CV statistic. As the best detection of endpoint is given by the W-Hotelling mean statistic, this statistic will be used to construct a windowed wavelet Hotelling polynomial regression. This latter can only identify the window containing endpoint phenomenon.

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Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression

Cited by 41 publications

References 12 publications

Real-time detection of wave profile changes

Real-time detection of wave profile changes

Scalable Statistical Monitoring of Fleet Data

Endpoint in plasma etch process using new modified w-multivariate charts and windowed regression

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