Design of exponential control charts based on average time to signal using a sequential sampling scheme

Yang, Jun; Yu, Haohui; Cheng, Yaqi; Xie, Min

doi:10.1080/00207543.2014.974848

Cited by 48 publications

(50 citation statements)

References 41 publications

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“…Huang et al studied two CUSUM schemes: one based on the max statistic combining maximum likelihood estimators (MLEs) of origin and scale parameters (CUSUM‐MLE‐Max chart) and the other based on the likelihood ratio statistic (CUSUM‐LR chart). For more details about the TBE control charts, the reader is referred to other works …”

Section: Introductionmentioning

confidence: 99%

Monitoring of time between events with a double generally weighted moving average control chart

Alevizakos

Koukouvinos

Lappa

2018

Quality & Reliability Eng

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Monitoring of time between events (TBE) instead of the number of events is used in high‐quality processes where the events occur rarely. This article presents a double generally weighted moving average control chart with a lower time‐varying control limit to monitor the TBE (regarded as DGWMA‐TBE chart). The design parameters of the proposed chart are provided, and through a simulation study, it is shown that the DGWMA‐TBE chart is more effective than the DEWMA and GWMA charts in detecting moderate to large shifts. Furthermore, the DGWMA‐TBE chart is very robust for the same range of shifts when the TBE observations follow a Weibull or a lognormal distribution. Finally, examples are also presented to enhance the performance of the proposed chart.

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Section: Introductionmentioning

confidence: 99%

Monitoring of time between events with a double generally weighted moving average control chart

Alevizakos

Koukouvinos

Lappa

2018

Quality & Reliability Eng

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“…Cheng and Chen incorporated the runs rules into the CQC chart design to study its ARL unbiasedness. Recently, Yang et al also adopted the same procedure as presented in Zhang et al to study the unbiasedness of the exponential chart based on the average time to signal performance measure with the following control limits:

\begin{matrix} UC L_{\exp - ATS} & = \frac{- \ln (α - p)}{λ_{0}} \\ LC L_{\exp - ATS} & = \frac{- \ln (1 - p)}{λ_{0}} \end{matrix}

where p can be obtained by solving (1 − p ) ln(1 − p ) − ( α − p ) ln( α − p ) = 0. They studied the phases I and II performances of the two‐sided control chart by using an unbiased estimator of the parameter and gave a comparison study to highlight the advantages/improvements of the new proposal.…”

Section: Time‐between‐events Control Chartsmentioning

confidence: 99%

An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

Quality & Reliability Eng

115

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Major difficulties in the study of high‐quality processes with traditional process monitoring techniques are a high false alarm rate and a negative lower control limit. The purpose of time‐between‐events control charts is to overcome existing problems in the high‐quality process monitoring setup. Time‐between‐events charts detect an out‐of‐control situation without great loss of sensitivity as compared with existing charts. High‐quality control charts gained much attention over the last decade because of the technological revolution. This article is dedicated to providing an overview of recent research and presenting it in a unifying framework. To summarize results and draw a precise conclusion from the statistical point of view, cross‐tabulations are also given in this article. Copyright © 2016 John Wiley & Sons, Ltd.

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“…Short production runs rarely offer enough data for estimation purposes Snoussi, El Ghourabi, & Limam, 2005;Zhang, Su, Li, & Wang, 2014). Many advanced statistical techniques were proposed and adapted for identify special causes of variation in the manufacturing process (Bettayeb, Bassetto, & Sahnoun, 2014;Chiu, 2015;Del Castillo & Montgomery, 1995;Graham, Mukherjee, & Chakraborti, 2012;Guh, 2010;Semino, Morretta, & Scali, 1996;Shi, Ceglarek, & Ding, 2000;Raza, Prasad, & Li, 2015;Yang, Yu, Cheng, & Xie, 2015;Zeifman & Ingman, 2003). All those approaches can solve the normality assumption violation, the non independent assumption violation, or even the problem of small size samples, but not all of them together.…”

Section: Introductionmentioning

confidence: 98%