EWMA Charts for Monitoring the Mean of Censored Weibull Lifetimes

Zhang, Lingyun; Chen, Gemai

doi:10.1080/00224065.2004.11980277

Cited by 92 publications

(81 citation statements)

References 7 publications

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“…ARL indicates the average number of subgroups until an out-of-control signal is given by a control chart. In this study, a Monte Carlo simulation approach with 10,000 iterations is used to approximate the RL distribution of the proposed MDEWMA chart following the methods of [6,[27][28][29][30]. Note that Kim [31] and Schaffer and Kim [32] indicate that 5000 replications are sufficient to estimate the ARL to an acceptable level of precision in many control chart settings.…”

Section: Performance Evaluationmentioning

confidence: 99%

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Abbasi

Miller

2013

Journal of Statistical Computation and Simulation

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Control chart is the most important statistical process control tool used to monitor changes in process location and dispersion. In this study, an EWMA control chart is proposed for efficient and robust monitoring of process dispersion. The proposed chart, namely the MDEWMA chart, is based on estimating the process standard deviation (σ ) using the mean absolute deviations (MD), taken from the sample median. The performance of the proposed chart has been compared with the EWMASR chart (a dispersion EWMA chart based on sample range) and MD chart (a Shewhart-type dispersion chart based on MD), under the existence and violation of normality assumption. It has been observed that the proposed MDEWMA chart is more efficient and robust when compared with both EWMASR and MD charts in terms of run length (RL) characteristics such as average RL, median RL and standard deviation of the RL distribution.

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Section: Performance Evaluationmentioning

confidence: 99%

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Abbasi

Miller

2013

Journal of Statistical Computation and Simulation

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“…Specify the value of ρ and generate a type II censored sample of size n from SEV(μ = 0, σ A = ρ). Computeσ using Equation (3). Determine if the sample is OOC under the standards given and retrospective scenarios.…”

Section: Scheme 1: Control Limits Onlymentioning

confidence: 99%

Monitoring the Weibull Shape Parameter with Type II Censored Data

Chan

Han

Pascual

2014

Qual. Reliab. Engng. Int.

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In this article, we introduce a method for monitoring the Weibull shape parameter β with type II (failure) censored data. The control limits depend on the sample size, the number of censored observations, the target average run length, and the stable value of β. The method assumes that the scale parameter α is constant during each sampling period, which is true under rational subgrouping. The proposed method utilizes the relationship between Weibull and smallest extreme value distribution. We propose an unbiased estimator of σ = 1/β as the monitoring statistic. We derive the control limits for one-sided and two-sided charts for several stable process average run lengths. We discuss two schemes, namely, the control-limits-only scheme and the control-limits-with-warning-lines scheme. The stable process average run length performance of the proposed charts is studied and compared with those of other charts for monitoring β under similar assumptions.

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“…Zhang and Chen [16] developed a control chart strategy when the random variable undergoing control represents a possibly censored Weibull distributed lifetime. They replace censored observations by their conditional expectation under the in-control conditions and apply a EWMA scheme.…”

Section: Frameworkmentioning

confidence: 99%

Control Charts for Radial Error

McCool

2006

Quality Technology & Quantitative Management

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In applications such as the drilling of holes or placement of components at a targeted location, the quality characteristic of interest is the radial distance of the center from its desired location. Control charts for radial error are established for the case where the center displacement errors in two orthogonal directions are normally and independently distributed with the same variance about the true center location. It is shown that a second chart based on the ratio of individual observed values of radial error can be used to distinguish between a radial shift in the means of the error distributions and an increase in their variability.

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EWMA Charts for Monitoring the Mean of Censored Weibull Lifetimes

Cited by 92 publications

References 7 publications

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

MDEWMA chart: an efficient and robust alternative to monitor process dispersion

Monitoring the Weibull Shape Parameter with Type II Censored Data

Control Charts for Radial Error

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