Monitoring of Time‐Between‐Events with a Generalized Group Runs Control Chart

Yen, Fang Yen; Khoo, Michael B. C.; Teh, Sin Yin; Xie, Min

doi:10.1002/qre.1789

Cited by 19 publications

(9 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shafae et al studied the performance of CUSUM control charts to monitor decreases in the TBE mean for Weibull distributed TBE observations. Fang et al proposed a generalized group runs TBE control chart to monitor the mean TBE of a homogenous Poisson failure process. Yang et al investigated Gamma charts for monitoring the time until the r th event for both the phases I and II.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…However, Exponential EWMA and Exponential CUMSUM charts outperform CQC-and CQC r -charts in case of small and moderate process shifts, and/or when these shifts can be accurately predicted [3,4,6].…”

Section: Reliability Monitoringmentioning

confidence: 99%

Control Charts for Monitoring the Reliability of Multi-State Systems. (Dept. M ( Production ) )

Genada

Hussein

Abdel-Shafi

2020

Bulletin of the Faculty of Engineering. Mansoura University

View full text Add to dashboard Cite

Control charts are widely used tools for monitoring process behavior. However, for high-quality processes, using traditional Shewhart control charts is not appropriate. Thus, several types of control charts have been established for monitoring high-quality processes based on the Exponential and Weibull distributions. These charts were later adopted for reliability monitoring. So far, these charts have been dedicated for monitoring the reliability of binary-state systems, which have only two levels of performance-functioning or failed. On the other hand, multi-state systems exhibit different levels of performance. This paper introduces two new types of control charts for monitoring the reliability of multi-state systems. Both types monitor can monitor different state transitions with different rates using the same control limits. The first type is the Angular Limits Control (ALC) chart. The ALC chart depicts different system-state distinctively but with the same probability control limits. To achieve this, the chart uses probability control limits with different angles of inclination. The second type is the Transformed Exponential Control (TEC) chart. The TEC chart uses a transformed form of the Exponential distribution as a model for the time to fail.

show abstract

“…Their proposal outperforms in detection of small‐to‐moderate shifts. Recently, Fang et al extended the work of Fang et al and proposed a generalized group run TBE chart named G G R g − T r (where r denotes the time until the r ‐th event occurrence and g denotes the index of the CRL group), to monitor the mean TBE of a homogenous Poisson process. With the help of a numerical study, they showed that the choices of r = 1 or r = 2 are good for the detection of a large‐sized shift, while r = 3 or r = 4 is appropriate for small‐to‐moderate shifts.…”

Section: Time‐between‐events Control Chartsmentioning

confidence: 99%

An Overview of Control Charts for High‐quality Processes

Ali

Pievatolo

Göb

2016

Quality & Reliability Eng

113

View full text Add to dashboard Cite

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.

show abstract

Monitoring of Time‐Between‐Events with a Generalized Group Runs Control Chart

Cited by 19 publications

References 50 publications

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

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

Control Charts for Monitoring the Reliability of Multi-State Systems. (Dept. M ( Production ) )

An Overview of Control Charts for High‐quality Processes

Contact Info

Product

Resources

About