Design of a Control Chart Using Extended EWMA Statistic

Naveed, Muhammad; Azam, Muhamma; Khan, Nasrullah; Aslam, Muhammad

doi:10.3390/technologies6040108

Cited by 39 publications

(55 citation statements)

References 11 publications

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“…The EEWMA control chart was proposed by Neveed et al [8]. It is developed from the EWMA control chart.…”

Section: Extended Exponentially Weighted Moving Average (Extended Ewm...mentioning

confidence: 99%

“…In addition, Patel et al [7]-proposed the Modified Exponentially Weighted Moving Average (Modified EWMA) chart that is effective at detecting small size shift quickly for observations both autocorrelation and independently normally distribution. Later, Neveed et al [8] proposed the Extended Exponentially Weighted Moving Average (EEWMA) chart that performed better than other control charts for detecting small shifts in the mean of a monitored process.…”

Section: Introductionmentioning

confidence: 99%

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Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Karoon¹,

Areepong²,

Sukparungsee³

2022

Intelligent Automation &Amp; Soft Computing

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Extended Exponentially Weighted Moving Average (Extended EWMA or EEWMA) control chart is one of the control charts which can quickly detect a small shift. The average run length (ARL) measures the performance of control chart. Due to the derivation of the explicit formulas for ARL on the EEWMA control chart for the autoregressive AR(p) process has not previously been reported. The aim of the article is to derive explicit formulas of ARL using a Fredholm integral equation of the second kind on EEWMA control chart for Autoregressive process, as AR(2) and AR(3) processes with exponential white noise. The accuracy of the solution obtained with the EEWMA control chart was compared to the numerical integral equation (NIE) method and extended to compare performance with Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) control charts. The results show that the ARL obtained by the explicit formula and the NIE method are hardly different but ARL of explicit formula is less the computational (CPU) time than ARL of NIE method. The performance of EEWMA control chart is better than the CUSUM and EWMA control charts for all situations except when the large shift sizes the EEWMA control chart performed as well as the EWMA control chart for AR(2) and AR(3) processes. And then, the EEWMA control chart is also extended to compare efficiency of EEW-MA control chart with various λ. An exponential smoothing parameter of 0.05 is recommended. In addition, the simulation study, and efficacy illustration with real data on new COVID-19 cases in Thailand and Vietnam provided similar results.

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“…The EEWMA control chart was proposed by Neveed et al [8]. It is developed from the EWMA control chart.…”

Section: Extended Exponentially Weighted Moving Average (Extended Ewm...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Karoon¹,

Areepong²,

Sukparungsee³

2022

Intelligent Automation &Amp; Soft Computing

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“…When the distribution of the process is normal, the EEWMA control chart was introduced by Naveed et al [17]. The EEWMA control chart by Naveed et al [17] is given as…”

Section: The Traditional Extended Exponentially Weighted Moving Averages (Eewma) Control Chartmentioning

confidence: 99%

“…Assuming E γMMLM = γ . The EEWMA statistic using MMLM of the shape parameter of the RPFD using Zaka et al [1] and Naveed et al [17] is stated by…”

Section: Proposed Eewma Control Chart Using Mmlmmentioning

confidence: 99%

Error Detection and Pattern Prediction Through Phase II Process Monitoring

Zaka¹,

Jabeen²,

Khan³

2022

Computers, Materials &Amp; Continua

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The continuous monitoring of the machine is beneficial in improving its process reliability through reflected power function distribution. It is substantial for identifying and removing errors at the early stages of production that ultimately benefit the firms in cost-saving and quality improvement. The current study introduces control charts that help the manufacturing concerns to keep the production process in control. It presents an exponentially weighted moving average and extended exponentially weighted moving average and then compared their performance. The percentiles estimator and the modified maximum likelihood estimator are used to constructing the control charts. The findings suggest that an extended exponentially weighted moving average control chart based on the percentiles estimator performs better than exponentially weighted moving average control charts based on the percentiles estimator and modified maximum likelihood estimator. Further, these results will help the firms in the early detection of errors that enhance the process reliability of the telecommunications and financing industry.

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“…In the present paper, we have applied the nonparametric test like statistical process control (SPC) method to analyze temperature and precipitation across India. This nonparametric method is widely used in detecting the smaller shift in the climatic change (Naveed et al 2018 ; Justin et al 2012 ; Eyvazian et al 2008 ). By exploring the literature, we found that no study as of yet utilizes the SPC chart for comparing and monitoring climatic variables.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal dynamics of temperature and precipitation with reference to COVID-19 pandemic lockdown: perspective from Indian subcontinent

Sengupta

Mohinuddin

Arif

2021

Environ Dev Sustain

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This study exclusively focuses on spatial and temporal change of temperature and precipitation before and after COVID-19 lockdown and also examines the extent of their variation and the spatial relationship between them. Our main objective is to analyze the spatiotemporal changes of two climatic variables in Indian subcontinent for the period of 2015-2020. Monthly precipitation and temperature data are collected from NOAA and NASA for January to May month across the four zones (northeast, northwest, central, and peninsular zone) of India. To conduct a zone-wise statistical analysis, we have adopted statistical process control (SPC) methods like exponentially weighted moving average (EWMA) control charts, individual charts (I-Chart) to detect the shift in temperature and precipitation over the study period and Pearson correlation coefficient applied to measure the spatial association between the two variables. The findings revealed that temperature parameter has experienced a lot of positive and negative trends in the span of 6 years and detected a weak to moderate negative correlation in many parts of the country in April 2020 after 2016. This study also identified a weak negative correlation mainly in NE zone in 2020 after 2017. This research provides vital scientific contribution to the effects of monthly temperature and precipitation before and after COVID-19 pandemic lockdown.

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Design of a Control Chart Using Extended EWMA Statistic

Cited by 39 publications

References 11 publications

Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Exact Run Length Evaluation on Extended EWMA Control Chart for Autoregressive Process

Error Detection and Pattern Prediction Through Phase II Process Monitoring

Spatiotemporal dynamics of temperature and precipitation with reference to COVID-19 pandemic lockdown: perspective from Indian subcontinent

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