Drift time detection and adjustment procedures for processes subject to linear trend

Fahmy, Hanan; Elsayed, Elsayed A.

doi:10.1080/00207540500410242

Cited by 23 publications

(7 citation statements)

References 17 publications

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“…Hawkins et al [13] provided an unknown parameter change point method for determining step changes in the process mean when the process parameters are unknown. Fahmy and Elsayed [8,9] showed that in comparison with the Cumulative sum CUSUM (Page [24]) and exponentially weighted moving average EWMA (Nishina [20]) control charts, the MLE approach offers a good overall performance in detection mean large size shifts. Amiri and Allahyari [4] offered an overview of change point estimation in control charts and studied and analyzed the gaps in change point issues and introduced frequent directions for future research.…”

Section: Related Workmentioning

confidence: 99%

A type-2 fuzzy-statistical clustering approach for estimating the multiple change points in a process mean with monotonic change

Zarandi

Najafi

2014

Int J Adv Manuf Technol

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Identifying the real time of the change in a process, recognized as change point problem, simplifies the removal of change causes. In most of the change point models, the existence of different uncertainty levels is either ignored or paid a little attention to. This paper tries to address an appropriate model to estimate the multiple change points in a monotonic change process in an uncertain condition and for a known number of change points. In this regard, a novel set of membership functions and a novel objective function, based on using interval type-2 fuzzy sets, are introduced in a fuzzy-statistical clustering approach. The proposed approach, in addition to the ability of managing various amount of uncertainty, is free from the distribution of the process variables, and in the case of existence, a certain variable distribution, it is independent from it. Finally, extensive simulation studies are conducted to evaluate the performance of the proposed approach in simple step change, multiple step change, and linear trend change in the presence of isotonic change in the process mean. The results are compared with some of the powerful change point approaches.

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Section: Related Workmentioning

confidence: 99%

A type-2 fuzzy-statistical clustering approach for estimating the multiple change points in a process mean with monotonic change

Zarandi

Najafi

2014

Int J Adv Manuf Technol

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“…An example of a drift change can occur when manufacturing equipment begins to wear out. Examples of this type of change are given in Perry, 9 Perry et al, 10 Fahmy and Elsayed, 11 and Perry and Pignatiello. 12 In monotonic changes, the type of change is unknown a priori, but the direction of shifts is the same, increasing or decreasing.…”

Section: Change Point Modelmentioning

confidence: 99%

Change Point Estimation Methods for Control Chart Postsignal Diagnostics: A Literature Review

Amiri

Allahyari

2011

Quality & Reliability Eng

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Control charts are the most popular monitoring tools used to distinguish between special (assignable) and common causes of variation and to detect any changes in processes. The time that a control chart gives an out‐of‐control signal is not the real time of change. The actual time of the change is called the change point. Knowing the real time of the change will help and simplify finding the assignable causes of the signal, which may be the result of a shift in the process mean or change in process variability. This article gives an overview of change point estimation in control charts, provides a classification scheme, and describes the research that has previously appeared in the literature. In addition, a gap analysis in this area provides direction for future research. Copyright © 2011 John Wiley & Sons, Ltd.

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“…While Samuel et al (1998a, b), Pignatiello and Samuel (2001), Ghazanfari et al (2008), Perry et al ( , 2007, Perry and Pignatiello (2005, Fahmy and Elsayed (2006), and Noorossana and Shadman (2009) proposed procedures to estimate the change point in the parameters of univariate distributions, Nedumaran et al (2000), Atashgar and Noorossana (2010), Niaki and Khedmati (2012, 2014a, and Sullivan and Woodall (2000) considered change-point estimations in the parameter vectors of multivariate distributions. However, in some applications, the quality of a process can be better characterized by a relationship between a response variable and one or more predictors.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Identifying the time of a step change in AR(1) auto-correlated simple linear profiles

Khedmati

Niaki

2015

J Ind Eng Int

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Assuming a first-order auto-regressive model for the auto-correlation structure between observations, in this paper, a transformation method is first employed to eliminate the effect of auto-correlation. Then, a maximum likelihood estimator (MLE) of a step change in the parameters of the transformed model is derived and three separate EWMA control charts are used to monitor the parameters of the profile. The performance of the proposed change-point estimator is next compared to the one of the built-in change-point estimator of EWMA control chart through some simulation experiments. The results show that the proposed MLE of the change point accurately estimates the true change point and outperforms the built-in estimator of EWMA chart for almost all shift values and auto-correlation coefficients, while the built-in estimator of EWMA chart, in general, underestimates the true change point.

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Drift time detection and adjustment procedures for processes subject to linear trend

Cited by 23 publications

References 17 publications

A type-2 fuzzy-statistical clustering approach for estimating the multiple change points in a process mean with monotonic change

A type-2 fuzzy-statistical clustering approach for estimating the multiple change points in a process mean with monotonic change

Change Point Estimation Methods for Control Chart Postsignal Diagnostics: A Literature Review

Identifying the time of a step change in AR(1) auto-correlated simple linear profiles

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