An adaptive max-type multivariate control chart by considering measurement errors and autocorrelation

Sabahno, Hamed

doi:10.1080/00949655.2023.2214830

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Studies such as Sabahno et al 1 have shown that the VP scheme, in which all the chart parameters are allowed to vary, is the best-performing scheme. For some notable works regarding adding different adaptive schemes to different control charts, we refer interested readers to Sabahno et al 1 – 4 , Sabahno & Celano 5 , and Sabahno 6 .…”

Section: Introductionmentioning

confidence: 99%

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles

Sabahno,

Eriksson

2024

Sci Rep

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Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts’ performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice.

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Section: Introductionmentioning

confidence: 99%

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles

Sabahno,

Eriksson

2024

Sci Rep

Self Cite

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“…A single-chart scheme is usually preferred due to its simplicity, considering only one control chart should be administrated in a single-chart scheme in comparison to the double-chart scheme, in which two control charts should be administrated at the same time. Reynolds and Gyo-Young [1], Hawkins and Maboudou-Tchao [2], and Zhang and Chang [3] studied double-chart cases, and Khoo [4], Zhang et al [5], Wang et al [6], Sabahno et al [7][8][9], Sabahno and Khoo [10], and Sabahno [11] investigated single-chart cases. Sabahno et al [9] proposed new memory-less statistical control charts with fixed and adaptive design parameters (sample size, sampling interval, and control limits) for simultaneous monitoring of multivariate normal process parameters.…”

Section: Introductionmentioning

confidence: 99%

New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification

Sabahno,

Niaki

2023

Mathematics

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Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training methods are used for the proposed ML structures. In addition, two different process control scenarios are utilized. In one, the goal is only the detection of the out-of-control situation. In the other one, the identification of the responsible variable (s)/process parameter (s) for the out-of-control signal is also an aim (detection–identification). After developing the ML control charts for each scenario, we compare them to one another, as well as to the most recently developed statistical control charts. The results show significantly better performance of the proposed ML control charts against the traditional memory-less statistical control charts in most compared cases. Finally, an illustrative example is presented to show how the proposed scheme can be implemented in a healthcare process.

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The economic production quantity model for an imperfect production process by control chart design

Nakhaeinejad

2024

Journal of Statistical Computation and Simulation

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An adaptive max-type multivariate control chart by considering measurement errors and autocorrelation

Cited by 3 publications

References 37 publications

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles

Variable parameters memory-type control charts for simultaneous monitoring of the mean and variability of multivariate multiple linear regression profiles

New Machine-Learning Control Charts for Simultaneous Monitoring of Multivariate Normal Process Parameters with Detection and Identification

The economic production quantity model for an imperfect production process by control chart design

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