About Shewhart control charts to monitor the Weibull mean based on a Gamma distribution

Vasconcelos, Renata M. R.; Quinino, Roberto da Costa; Ho, Linda Lee; Cruz, Frederico R. B.

doi:10.1002/qre.3200

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…Nevertheless, generalities of the exponential distribution (like Weibull and Gamma) are beneficial in showing reliability [ [197] , [198] , [199] , [200] , [201] , [202] , [203] , [204] , [205] ]. Three-parameter Weibull distributions are beneficial in the sense that their logic implies that verge parameters are considered in modelling [ [206] , [207] , [208] ]. This section will review the three-parameter Weibull Distribution.…”

Section: Overview Of Weibull Analysis (Wa)mentioning

confidence: 99%

Weibull analysis of ceramics and related materials: A review

Osuchukwu,

Salihi,

Ibrahim

et al. 2024

Heliyon

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Section: Overview Of Weibull Analysis (Wa)mentioning

confidence: 99%

Weibull analysis of ceramics and related materials: A review

Osuchukwu,

Salihi,

Ibrahim

et al. 2024

Heliyon

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“…The coefficients of control limits for various sample sizes and truncation coefficients depend on the target ARL value and shift coefficient explored and computed using simulation and real-life examples from Refs. 10 , 11 explored Shewhart control charts which are used to monitor the Weibull mean based on the gamma distribution. Another control chart was proposed under the assumption of Weibull distribution by Ref.…”

Section: Introductionmentioning

confidence: 99%

Optimized two-stage time-truncated control chart for Weibull distribution

Ali,

Jorge,

Aslam

et al. 2024

Sci Rep

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In this article, an attribute control chart is proposed when the lifetime of a product follows a Weibull distribution in two-stage sampling, which is based on the number of failures from a truncated life test. The coefficients of the proposed double sampling attribute control chart and the test duration are determined so that the average run length when the process is in control is close to the target value. An overview is reported on how double sampling np control charts work. Tables reporting the out-of-control average run lengths are given for various shift parameters. A case study is given to illustrate the proposed control chart for industrial use. A comparison of two-stage and single-stage sampling of failure of products is discussed.

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“…Asif and Noor-ul Amin [23] applied the idea of an adaptive EWMA control chart in their study and discussed a novel adaptive risk-adjusted EWMA control chart by utilizing the AFT regression. Vasconcelos et al [24] developed a control chart with Weibull-distributed quality characteristics for monitoring a process mean. They used transformation to make use of a gamma distribution to analytically determine control limits to monitor the process mean.…”

Section: Introductionmentioning

confidence: 99%

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

et al. 2023

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Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturingprocess. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study.

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About Shewhart control charts to monitor the Weibull mean based on a Gamma distribution

Cited by 7 publications

References 46 publications

Weibull analysis of ceramics and related materials: A review

Weibull analysis of ceramics and related materials: A review

Optimized two-stage time-truncated control chart for Weibull distribution

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

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