Monitoring and control of beta-distributed multistage production processes

Kim, S.; Kim, J.; Jeong, Myong K.; Al-Khalifa, Khalifa N.; Elsayed, Elsayed A.

doi:10.1080/16843703.2017.1335495

Cited by 11 publications

(19 citation statements)

References 39 publications

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“…However, the estimation of Beta parameters is not applicable by using GLMs; Ferrari et al [21] investigated a Beta regression model instead of the GLMs and derived a closed form expression for deviance residual. Recently, the deviance residual in Beta regression is applied in a multistage SPC chart [11,12].…”

Section: Model-based Multistage Process Control Charts With Beta Distributed Output Variablesmentioning

confidence: 99%

“…The multistage process model with Beta distributed output variables is analyzed through the Beta regression model [11,12,21]. The cumulative Beta distribution function with two shape parameters α and β is expressed as follows:…”

Section: Fig 1 Multistage or Cascade Processmentioning

confidence: 99%

“…Then, contaminants such as sulfur and carbon dioxide that are contained in natural gas are removed. Nitrogen will be effectively removed by a molecular gate, and the final product, LNG, will be obtained after de-methanizing that extracts the methane from other gases [12,37].…”

Section: Case Study: Monitoring An Lng Processmentioning

confidence: 99%

“…Multistage processes with Beta distributed output variables are also generally prevalent in the real industry. Recently, multistage SPC charts based on deviance residual with Beta distributed output variables are proposed [11,12].…”

Section: Introductionmentioning

confidence: 99%

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Variable selection-based SPC procedures for high-dimensional multistage processes

2019

Journal of Systems Engineering and Electronics

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Monitoring high-dimensional multistage processes becomes crucial to ensure the quality of the final product in modern industry environments. Few statistical process monitoring (SPC) approaches for monitoring and controlling quality in highdimensional multistage processes are studied. We propose a deviance residual-based multivariate exponentially weighted moving average (MEWMA) control chart with a variable selection procedure. We demonstrate that it outperforms the existing multivariate SPC charts in terms of out-of-control average run length (ARL) for the detection of process mean shift.

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Section: Model-based Multistage Process Control Charts With Beta Distributed Output Variablesmentioning

confidence: 99%

Section: Fig 1 Multistage or Cascade Processmentioning

confidence: 99%

Section: Case Study: Monitoring An Lng Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variable selection-based SPC procedures for high-dimensional multistage processes

2019

Journal of Systems Engineering and Electronics

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“…Asadayyoobi and Niaki presented a model‐based control procedure to monitor survival data (reliability data) under type I censoring based on a likelihood ratio test derived from a change point model. Kim et al considered beta‐distributed data in multistage processes and proposed two approaches for optimal process monitoring.…”

Section: Introductionmentioning

confidence: 99%

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

Keshavarz

Asadzadeh

Niaki

2019

Quality & Reliability Eng

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In recent years, much attention has been given to monitoring multistage processes in order to effectively improve the product reliability. To this end, the output of the process is investigated under special circumstances, and the values corresponding to reliability‐related quality characteristic are measured. However, analyzing reliability data is quite complicated because of their unique features such as being censored and obeying nonnormal distributions. A more sophisticated picture arises when the observations of the process are autocorrelated in some cases, which makes the application of previous control procedures futile. In this paper, the accelerated failure time (AFT) regression models have been modified in order to account for autocorrelated data. Then, a cumulative sum (CUSUM) control chart and an exponentially weighted moving average (EWMA) control chart based on conditional expected values have been proposed to monitor the quality variable with Weibull distribution while taking the effective covariates into consideration. Extensive simulation studies reveal that the CUSUM control chart outperforms its counterpart in detecting out‐of‐control conditions. Finally, a real case study in a textile industry has been provided to investigate the application of the CUSUM control scheme.

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Deviance residual‐based control charts for monitoring the beta‐distributed processes

Hwang

2021

Quality & Reliability Eng

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Beta‐distributed process outputs are common in manufacturing industry because they range from 0 to 1 based on inputs like yield. Under the normality assumption, Shewarts control charts and Hotelling's control charts based on the deviance residual have been applied to monitor the process mean of the beta‐distributed process outputs. The normality assumption can be violated according to the shape of the beta distribution. Therefore, without the normality assumption, we propose antirank control charts, exponentially weighted moving average (EWMA) control charts and cumulative sum (CUSUM) control charts. The proposed control charts outperform the existing control charts in the experimental results. The previous research has been focused on monitoring the process mean only. For the first time, in order to monitor the process variance of the beta‐distributed process outputs, we propose the multivariate exponentially weighted mean squared deviation (MEWMS) chart, the first norm distance of the MEWMS deviation from its expected value (MEWMSL1) chart, the chart based on MEWMS deviation with the approximated distribution of trace (MEWMSAT), the multivariate trace sum squared deviation (MTSSD) chart and the multivariate matrix sum squared deviation (MMSSD) chart based on the deviance residual. The proposed control charts are compared and recommended in terms of the experimental results. This research can be a guideline for practitioners who monitor the deviance residual.

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Monitoring and control of beta-distributed multistage production processes

Cited by 11 publications

References 39 publications

Variable selection-based SPC procedures for high-dimensional multistage processes

Variable selection-based SPC procedures for high-dimensional multistage processes

Controlling autocorrelated data in multistage manufacturing processes with an application to textile industry

Deviance residual‐based control charts for monitoring the beta‐distributed processes

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