A high-dimensional control chart for monitoring process variability under gauge imprecision effect

Jafari, Mostafa; Maleki, Mohammad Reza; Salmasnia, Ali

doi:10.1007/s11740-022-01166-3

Cited by 5 publications

(2 citation statements)

References 38 publications

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“…Additionally, a multitude of dispersion control charts have been introduced in the literature. These include the works of Reynolds and Stoumbos [18], Castagliola et al [19], Castagliola, Celano and Fichera [20], Shu and Jiang [21], Abbasi et al [22], Zaman et al [23], Zhou, Zhou and Geng [24], Sanusi et al [25], Ali and Haq [26,27], Haq [28,29], Abbas et al [30], Huang, Lu and Chen [31], Riaz et al [32], Mahadik, Godase and Teoh [33], Arshad, Noor-ul-Amin and Hanif [34], Haq and Razzaq [35], Godase et al [36], Ajibade et al [37], Yang, Chen and Lin [38] and Jafari, Maleki and Salmasnia [39].…”

Section: Introductionmentioning

confidence: 99%

Simulation modeling with memory-type control charts for monitoring the process variability

Chatterjee,

Hached,

Koukouvinos

et al. 2023

J. Appl. Artif. Intell.

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Memory-type control charts, renowned for their effectiveness in identifying small deviations in the process variance, are commonly used to monitor the process variability. In this article, we introduce a new tool, the Quadruple Exponentially Weighted Moving Average (QEWMA) chart, which is designed for the specific purpose of monitoring changes in the process variability. We refer to this chart as the -QEWMA chart. The performance of the -QEWMA chart is assessed through an extensive series of Monte-Carlo simulations, carefully considering the run-length distribution. Comparing it with other well-known memory-type charts, it becomes evident that the -QEWMA chart excels in its ability to effectively detect small shifts in the process dispersion. To illustrate the practical application of this chart, we provide an example.

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Section: Introductionmentioning

confidence: 99%

Simulation modeling with memory-type control charts for monitoring the process variability

Chatterjee,

Hached,

Koukouvinos

et al. 2023

J. Appl. Artif. Intell.

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show abstract

“…They showed that their proposed control chart works better than L 2 -type and L  -type control charts. Jafari et al [14] proposed an adaptive thresholding LASSO (ATL) control charting method for Phase II monitoring of high-dimensional covariance matrices by considering the impact of gauge inaccuracy. The performance of control chart can be severely dependent on sampling strategies specially when even small delays in detection of assignable causes imposes significant cost on the production system.…”

Section: Introductionmentioning

confidence: 99%

A generalized multiple dependent state sampling chart based on ridge penalized likelihood ratio for high-dimensional covariance matrix monitoring

2022

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Online monitoring of high-dimensional processes variability in which the number of variables is larger than the sample size is a challenging issue for quality practitioners because the sample covariance matrix is not invariable. To deal with this challenge, a generalized multiple dependent state sampling (GMDS) chart based on ridge penalized likelihood ratio (RPLR) statistic is developed for Phase II monitoring of multivariate process variability under high-dimensional setting. The developed control chart benefits from three advantages: (1) departing from the conventional covariance matrix charts, it can be efficiently employed for both spars and nonspars covariance matrices; (2) it is able to detect spars shift patterns in which only a few covariance matrix elements are deviated from their nominal values; and (3) it outperforms the detectability of the RPLR chart in terms of average run length (ARL) and standard deviation of run length (SDRL). The performance of RPLR, MDS-RPLR, and GMDS-RPLR charts are compared using extensive simulation studies by considering different diagonal and/or offdiagonal covariance matrix disturbances. Moreover, sensitivity analysis are provided to analyze how the number of process variables and GMDS parameter affect the run length properties of the developed chart.

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Evaluation of gauges in measurement systems

Dalalah

2023

Prod. Eng. Res. Devel.

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A high-dimensional control chart for monitoring process variability under gauge imprecision effect

Cited by 5 publications

References 38 publications

Simulation modeling with memory-type control charts for monitoring the process variability

Simulation modeling with memory-type control charts for monitoring the process variability

A generalized multiple dependent state sampling chart based on ridge penalized likelihood ratio for high-dimensional covariance matrix monitoring

Evaluation of gauges in measurement systems

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