Design of a New Tukey MA-DEWMA Control Chart to Monitor Process and its Applications

Taboran, Rattikarn; Sukparungsee, Saowanit; Areepong, Yupaporn

doi:10.1109/access.2021.3098172

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…where the sample size is set to 𝑁 = 𝟏 𝑇 𝒂 𝑙𝑘,𝑡 (𝑔) = 1 . As Taboran et al [30] pointed out, a monitoring statistic becomes more sensitive to small and moderate magnitude of process shifts when current and past samples are combined with a framework of exponentially weighted moving average (EWMA). Assume the smoothing parameter 0 < 𝜆 < 1, the combined observations will be…”

Section: Proposed Control Chartmentioning

confidence: 99%

An Efficient Control Chart for Monitoring Unweighted Networks With Overlapping Communities

Wei,

2024

IEEE Access

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In recent years, detecting anomalies in networks has attracted much attention in the literature. Researchers have developed various statistical process control (SPC) techniques to monitor the communication level within the networks. It is quite common to see the nodes belonging to some densely connected communities or subgroups and the nodes within a community communicate with each other more frequently than with nodes from other communities. However, we can find scarce SPC techniques that consider the multiple community labels and heterogeneity of nodes simultaneously. It is necessary to overcome this drawback to improve the change detection efficiency. This article establishes a new mixedmembership stochastic block model to characterize the multiple community labels and heterogeneity of nodes simultaneously. Then a new control chart is developed based on generalized likelihood ratio test (GLRT). The proposed control chart can be easily implemented in practice and avoids constant parameter estimation in Phase II monitoring. Numerous simulation studies and a real example show the advantages of proposed chart over existing ones.INDEX TERMS change detection, unweighted directed networks, stochastic block model, statistical process control.

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Section: Proposed Control Chartmentioning

confidence: 99%

An Efficient Control Chart for Monitoring Unweighted Networks With Overlapping Communities

Wei,

2024

IEEE Access

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“…For example, Abbas et al [1] use the exponentially weighted moving average (EWMA) statistic as the input into a cumulative sum (CUSUM) chart in their approach. Similarly, the approach of Taboran et al [2] uses a moving average (MA) as the input into a double EWMA chart. Knoth et al [3] have shown that the justification of some of these ad hoc methods is inadequate.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the approach of Taboran et al. [2] uses a moving average (MA) as the input into a double EWMA chart. Knoth et al.…”

Section: Introductionmentioning

confidence: 99%

The effects of large round‐off errors on the performance of control charts for the mean when the quality characteristic is normally distributed with a known variance

Diamanta,

Michal,

Ofer

et al. 2023

IET Science Measure & Tech

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This research discusses the effects of large round‐off errors on the performance of control charts for means when a process is normally distributed with a known variance and a fixed sample size. Quality control in practice uses control charts for means as a process monitoring tool, even when the data is significantly rounded. The objective of this research is to demonstrate how ignoring the round‐off errors and using a standard Shewhart chart affects the quality control of a measured process. The first part of the research includes theoretical calculations for estimating the values of alpha and beta, relating to the unrounded data and the large‐rounded data. For the rounded data, normality can no longer be assumed because the data is discrete; therefore, the multinomial distribution is used. The results show that given an in‐control process, alpha indicates that false alarms are more frequent, whereas given an out‐of‐control process, the influence on beta is minor and inconsistent. For some rounding levels, there is a decline in the control chart performances, and in others, there is an improvement. In the second part, a simulation study is used to evaluate the performances of the control chart based on a single sample, checking whether the conclusion (reject or fail to reject) for a sample is consistent for rounded and unrounded data. The results of the simulation match the theoretical calculations.

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“…Riaz et al [ 25 ] designed mixed Tukey EWMA-CUSUM chart (MEC-TCC). Taboran et al [ 26 , 27 ] investigated a Tukey MA-EWMA control chart and a Tukey MA-DEWMA control chart for the monitor mean process, respectively. Khaliq et al [ 28 ] suggested a median-based design for Tukey and Tukey-EWMA control charts under subgrouping, and etc.…”

Section: Introductionmentioning

confidence: 99%

Performance of new nonparametric Tukey modified exponentially weighted moving average—Moving average control chart

2022

Self Cite

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Control charts are an amazing and essential statistical process control (SPC) instrument that is commonly used in monitoring systems to detect a specific defect in the procedure. The mixed Tukey modified exponentially weighted moving average - moving average control chart (MMEM-TCC) with motivation detection ability for fewer shifts in the process mean under symmetric and non-symmetric distributions is proposed in this paper. Average run length (ARL), standard deviation of run length (SDRL), and median run length (MRL) were used as efficiency criteria in the Monte Carlo simulation, and their efficiency was compared to existing control charts. Furthermore, the expected ARL (EARL) is a method for evaluating the performance of control charts beyond a specific range of shift sizes. The distinguishing feature of the proposed chart is that it performs efficiently in detecting small to moderate shifts. There are applications for PM 2.5 and PM 10 data that demonstrate the performance of the proposed chart.

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Design of a New Tukey MA-DEWMA Control Chart to Monitor Process and its Applications

Cited by 9 publications

References 23 publications

An Efficient Control Chart for Monitoring Unweighted Networks With Overlapping Communities

An Efficient Control Chart for Monitoring Unweighted Networks With Overlapping Communities

The effects of large round‐off errors on the performance of control charts for the mean when the quality characteristic is normally distributed with a known variance

Performance of new nonparametric Tukey modified exponentially weighted moving average—Moving average control chart

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