A Mixed EWMA–CUSUM Control Chart for Weibull‐Distributed Quality Characteristics

Aslam, Muhammad

doi:10.1002/qre.1982

Cited by 36 publications

(25 citation statements)

References 31 publications

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“…Aslam proposed a MEC control chart for monitoring a downward shift in Weibull‐distributed TBE mean employing the following procedure. Draw a random sample of size 1, y i ( i = 1, 2, …), from the Weibull distribution.…”

Section: Four Control Chartsmentioning

confidence: 99%

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A comparison study of control charts for Weibull distributed time between events

Wang

Bizuneh

Abebe

2017

Quality & Reliability Eng

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Monitoring decreases in the mean of Weibull time between events data to address process quality deteriorations is an important task in reliability analysis. Two new control charts such as Weibull exponentially weighted moving average and mixed cumulative sum‐exponentially weighted moving average by transforming the Weibull data to the exponential data are proposed and compared with 2 existing control charts such as Weibull cumulative sum and mixed exponentially weighted moving average‐cumulative sum. The performance comparison provides a way to select a specific control chart in a given situation. The average run length and the standard deviation of the run length are used as performance measures. The relative mean index is also utilized to measure the overall performance. The smaller the value of the relative mean index, the better the performance of the control chart and vice versa. Two illustrative examples are provided to show the applications of the proposed control charts.

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Section: Four Control Chartsmentioning

confidence: 99%

“…Abbas et al proposed the mixed EWMA‐CUSUM (MEC) chart to monitor the mean shift of a normal process. Aslam presented the MEC chart for monitoring the downward shift for Weibull‐distributed quality characteristic with a fixed shape parameter. Haq presented a hybrid EWMA control chart by mixing 2 EWMA charts for a normal process.…”

Section: Introductionmentioning

confidence: 99%

A comparison study of control charts for Weibull distributed time between events

Wang

Bizuneh

Abebe

2017

Quality & Reliability Eng

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“…This simulation approach is commonly used when the exact form of the mean and other measures of the proposed process is not available. Many researchers have used the simulation approach for the effectiveness of control charts including among others Santos (2009), Abbasi and Miller (2013), , , Chananet et al (2014), Shu et al (2014), , Azam et al (2015) and Aslam (2016).…”

Section: Performance Evaluation Of the Proposed Chartmentioning

confidence: 99%

A Control Chart for Gamma Distribution using Multiple Dependent State Sampling

Aslam¹,

Arif²

2017

Industrial Engineering and Management Systems

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In this article, a control chart based on multiple dependent (or deferred) state sampling for the gamma distributed quality characteristic is proposed using the gamma to normal transformation. The proposed control chart has two pairs of control limits, which can be determined by considering the in-control average run length (ARL). The shift in the scale parameter of a gamma distribution is considered and the out-of-control ARL is evaluated. The performance of the proposed chart has been shown for different levels of the parameters of the proposed control chart. It is also shown that the proposed chart is better than the Shewhart chart in terms of ARLs. A case study with a real data has been included for the practical usage of the proposed scheme.

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“…Furthermore, Shafae et al considered CUSUM schemes for monitoring Weibull‐distributed time between events. Aslam introduced mixed EWMA‐CUSUM control chart for monitoring Weibull distributed quality characteristics. Erto et al studied a semiempirical Bayesian scheme to monitor Weibull percentiles.…”

Section: Introductionmentioning

confidence: 99%

Design and comparison of some Shewhart‐type schemes for simultaneous monitoring of Weibull parameters

Gong

Mukherjee

2018

Quality & Reliability Eng

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Weibull distribution is one of the most important probability models used in modeling time between events, system reliability, and particle sizes, among others. Therefore, efficiently and consequently monitoring certain changes in Weibull process is considered as an important research topic. Various statistical process monitoring schemes have been developed for monitoring different process parameters, including some for Weibull parameters. Most of these schemes are, however, designed to monitor and control a single process parameter, although there are two important model parameters for Weibull distribution. Recently, several researchers studied various schemes for jointly monitoring the mean and variance of a normally distributed process using a single plotting statistic. Nevertheless, there is still dearth of researches in joint monitoring of non‐normal process parameters. In this context, we develop some control schemes for simultaneously monitoring the scale and shape parameters of processes that follow the Weibull distribution. Implementation procedures are developed, and performance properties of various proposed schemes are investigated. We also offer an illustrative example along with a summary and recommendations.

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A Mixed EWMA–CUSUM Control Chart for Weibull‐Distributed Quality Characteristics

Cited by 36 publications

References 31 publications

A comparison study of control charts for Weibull distributed time between events

A comparison study of control charts for Weibull distributed time between events

A Control Chart for Gamma Distribution using Multiple Dependent State Sampling

Design and comparison of some Shewhart‐type schemes for simultaneous monitoring of Weibull parameters

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