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Journal articles on the topic 'Standard deviation chart'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Schoonhoven, Marit, and Ronald J. M. M. Does. "A Robust Standard Deviation Control Chart." Technometrics 54, no. 1 (2012): 73–82. http://dx.doi.org/10.1080/00401706.2012.648869.

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2

Phantu, Suganya, Yupaporn Areepong, and Saowanit Sukparungsee. "Analysis Process Dispersion Variation Tracked using a Mixed MA-EWMA Control Chart." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 19 (July 29, 2024): 217–26. http://dx.doi.org/10.37394/23203.2024.19.23.

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This study aims to develop a combined Moving Average - Exponentially Weighted Moving Average Control Chart with standard deviation based (MA-EWMAS chart) that can be used to identify changes in standard deviation in processes under a normal distribution. The average run length (ARL), standard deviation of run length (SRL), and median run length (MRL) are used to compare the performance of the proposed control chart with S, MAS, and EWMAS control charts. This benchmark is assessed using Monte Carlo (MC) simulations. Furthermore, actual data is used to apply the suggested control charts. For all

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3

Kalpesh, S. Tailor. "SAMPLE STANDARD DEVIATION(s) CHART UNDER THE ASSUMPTION OF MODERATENESS AND ITS PERFORMANCE ANALYSIS." International Journal of Research - Granthaalayah 5, no. 6 (2017): 368–77. https://doi.org/10.5281/zenodo.821537.

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Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters and which has properties similar to normal distribution. Mean deviation (δ) is a very good alternative of standard deviation (σ) as mean deviation is considered to be the most intuitively and rationally defined measure of dispersion. This fact can be very useful in the field of quality control to construct the control limits of the control charts. On the basis of this fact Naik V.D. and Tailor K.S. have proposed 3δ control limits. In

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4

Huang, Wei-Heng, Arthur B. Yeh, and Hsiuying Wang. "A control chart for the lognormal standard deviation." Quality Technology & Quantitative Management 15, no. 1 (2017): 1–36. http://dx.doi.org/10.1080/16843703.2017.1304044.

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Tailor, Kalpesh S. "SAMPLE STANDARD DEVIATION(s) CHART UNDER THE ASSUMPTION OF MODERATENESS AND ITS PERFORMANCE ANALYSIS." International Journal of Research -GRANTHAALAYAH 5, no. 6 (2017): 368–77. http://dx.doi.org/10.29121/granthaalayah.v5.i6.2017.2045.

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Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters and which has properties similar to normal distribution. Mean deviation (δ) is a very good alternative of standard deviation (σ) as mean deviation is considered to be the most intuitively and rationally defined measure of dispersion. This fact can be very useful in the field of quality control to construct the control limits of the control charts. On the basis of this fact Naik V.D. and Tailor K.S. have proposed 3δ control limits. In

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6

Shan, Tingting, Liusan Wu, and Xuelong Hu. "A Combined Upper-Sided Synthetic S2 Chart for Monitoring the Process Variance." Mathematical Problems in Engineering 2020 (June 24, 2020): 1–6. http://dx.doi.org/10.1155/2020/9102059.

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In order to monitor the process variance, this paper proposes a combined upper-sided synthetic S2 chart for monitoring the process standard deviation of a normally distributed process. This combined upper-sided synthetic S2 chart comprises a synthetic chart and an upper-sided S2 chart. The design and performance of the proposed chart are presented, and the steady-state average run length comparisons show that the combined upper-sided synthetic S2 chart outperforms the standard synthetic S2 chart as well as several run rules S2 charts, especially for larger shifts in the process variance.

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Sindhumol, Marangattu R., Michele Gallo, and Mamandur R. Srinivasan. "Monitoring Industrial Process using a Robust Modified Mean Chart." Austrian Journal of Statistics 48, no. 2 (2019): 1–13. http://dx.doi.org/10.17713/ajs.v48i1.765.

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Shewhart control chart is the most popular and widely used Statistical process Control tool to monitor process. It is developed under the assumption of independent and normally distributed process. In order to control process mean and standard deviation, robust estimator of these parameters can be better alternatives as charts based on that are more resistant to moderate changes in process distribution. Modified Maximum Likelihood Estimator (MMLE) for mean and standard deviation is a pair of statistics with good robust properties. Authors introduced these measures to control charting process a

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Mayashari, Mayashari, Erna Tri Herdiani, and Anisa Anisa. "COMPARISON OF CONTROL CHART X ̅ BASED ON MEDIAN ABSOLUTE DEVIATION WITH S." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 0737–50. http://dx.doi.org/10.30598/barekengvol18iss2pp0737-0750.

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A stable and controlled process will produce products of good quality following predetermined specifications. A control chart is one of the statistical tools that can be used to measure the stability of a product process in a controlled state. Control charts commonly used to evaluate the statistical control process are Shewhart control charts ( and ). The control chart is used to control the process, as seen from the average and variability of the process. If the data used is not normally distributed or there are outliers, then an alternative control chart, namely the Median Absolute Deviation

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Huang, Wei-Heng. "The Performance of S Control Charts for the Lognormal Distribution with Estimated Parameters." Sustainability 14, no. 24 (2022): 16582. http://dx.doi.org/10.3390/su142416582.

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Control charts, one of the powerful tools in statistical process control (SPC), are widely used to monitor and detect out-of-control processes in the manufacturing industry. Many researchers have pointed out the effects of using estimated parameters on the average run length (ARL) performance metric. Most of the previous literature has studied the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL) to evaluate the performance of control charts. In this article, we study the performance of three S control charts, the Shewhart S-chart, the

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MUHAMMAD, SAAD, IDREES MUHAMMAD DAWOOD, ANSARI ARSALAN, SAMI ABDUL, RAUF MUHAMMAD, and JAMIL ATIF. "Reduction of non-conforming through statistical process control charts in textile industry." Industria Textila 73, no. 05 (2022): 537–43. http://dx.doi.org/10.35530/it.073.05.202166.

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The textile industry of Pakistan is a growing sector that contributes to the economy. Pakistan exports depend heavily upon textile goods. A minor defect in the finished good can cause a major loss of the export goods. Due to the involving the number of workers checking the product repeatedly can be very expensive therefore the quality engineering techniques of Statistical Control Charts are used in the textile industry. This research study aims at developing process control charts for the textile industry in Pakistan. For this purpose, the textile industry was taken into consideration. P-chart

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11

Gardi, Chandrakant G., and Vikas B. Ghute. "Monitoring Dispersion in the Compressive Strength of Parts Using Group Runs Control Chart." Asian Journal of Probability and Statistics 27, no. 7 (2025): 192–202. https://doi.org/10.9734/ajpas/2025/v27i7785.

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Given the limited sensitivity of Shewhart-type control charts in detecting small shifts in the process mean, synthetic control charts have been proposed as a more effective alternative for enhancing detection performance. Group Runs (GR) control chart is a further extension of synthetic chart, which performs better than the synthetic chart. This study proposes a GR control chart based on the range, referred to as the GR-R chart, for monitoring process dispersion in normally distributed processes. With the help of extensive simulation, the proposed GR control chart (GR-R) is compared with synth

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Khalil, Umair, Tahira Saeed Khan, Walaa Ahmad Hamdi, Dost Muhammad Khan, and Muhammad Hamraz. "A robust cusum control chart for median absolute deviation based on trimming and winsorization." PLOS ONE 19, no. 5 (2024): e0297544. http://dx.doi.org/10.1371/journal.pone.0297544.

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Statistical quality control is concerned with the analysis of production and manufacturing processes. Control charts are process control techniques, commonly applied to observe and control deviations. Shewhart control charts are very sensitive and used for large shifts based on the basic assumption of normality. Cumulative Sum (CUSUM) control charts are effective for identifying that may have special causes, such as outliers or excessive variability in subgroup means. This study uses a CUSUM control chart problems structure to evaluate the performance of robust dispersion parameters. We invest

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Bhat, Sharada V., and Shradha S. Patil. "Shewhart Type Control Chart Based on Median for Location Parameter." INTERNATIONAL JOURNAL OF AGRICULTURAL AND STATISTICAL SCIENCES 20, no. 01 (2024): 275. http://dx.doi.org/10.59467/ijass.2024.20.275.

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Control charts are prominent tools in monitoring various variables of manufacturing processes. In this paper, the control chart based on sample median is proposed for process location parameter using Shewhart?s approach. The proposed control chart is developed under some distributions including normal distribution. Its performance is studied in terms of power, average run length (ARL) and standard deviation of run length (SDRL). Also, the effect of non normality on this control chart is discussed.. KEYWORDS :ARL, Control limits, Location parameter, Median, Non-normality, Power.

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14

Xue, Li, and Zhen He. "Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions." Mathematical Problems in Engineering 2021 (September 8, 2021): 1–14. http://dx.doi.org/10.1155/2021/6686426.

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The control chart and the maintenance management need to control process quality and reduce out-of-control cost. They are two key tools in the production process; however, they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these two concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, so that the small and middle shifts are detected slowly; (2) monitoring the mean and standard deviation separately, whereas, in real condition, the mean and standard deviation shoul

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15

Chen, Jen-Hsiang, and Shin-Li Lu. "An Enhanced Auxiliary Information-Based EWMA-t Chart for Monitoring the Process Mean." Applied Sciences 10, no. 7 (2020): 2252. http://dx.doi.org/10.3390/app10072252.

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The exponentially weighted moving average t chart using auxiliary information (AIB-EWMA-t chart) is an effective approach for monitoring small process mean shifts when the process standard deviation is unstable or poorly estimated. To further enhance the sensitivity of the AIB-EWMA-t chart, in this study, we propose an AIB generally weighted moving average (GWMA) t chart (AIB-GWMA-t chart) to monitor the process mean. The existing EWMA-t, GWMA-t, and AIB-EWMA-t charts are special cases of the AIB-GWMA-t chart. Numerical simulation studies indicate that the AIB-GWMA-t chart performs uniformly a

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16

Zhou, Jing Jia, Kok Haur Ng, Kooi Huat Ng, Shelton Peiris, and You Beng Koh. "Asymmetric Control Limits for Weighted-Variance Mean Control Chart with Different Scale Estimators under Weibull Distributed Process." Mathematics 10, no. 22 (2022): 4380. http://dx.doi.org/10.3390/math10224380.

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Shewhart charts are the most commonly utilised control charts for process monitoring in industries with the assumption that the underlying distribution of the quality characteristic is normal. However, this assumption may not always hold true in practice. In this paper, the weighted-variance mean charts are developed and their population standard deviation is estimated using the three subgroup scale estimators, namely the standard deviation, median absolute deviation and standard deviation of trimmed mean for monitoring Weibull distributed data with different coefficients of skewness. This stu

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17

M. Ali Hameed, Lamyaa. "Various Control Charts to Monitor the Quality of Resistant Cement Production in the Kabaisa Cement Factory for the Year 2023." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ) 56, no. 1 (2025): 534–44. https://doi.org/10.55562/jrucs.v56i1.48.

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Control charts are an effective tool for statistical control of production quality processes by drawing samples from the production randomly and at specific time periods. In this research, different control charts were used for the purpose of monitoring the quality of resistant cement production in the Kubaisa Cement Factory, which is located in Al - Anbar Governorate. The weights of bags of cement (resistant cement) (which are measured in kilograms) were monitored using a new packing device, and after monitoring production for a period of (50) days in the months of November and December for a

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18

Chang, Youngsoon. "Variable Sampling Interval X Control Chart Using Weighted Standard Deviation Method." Journal of the Korean Operations Research and Management Science Society 39, no. 1 (2014): 1–12. http://dx.doi.org/10.7737/jkorms.2014.39.1.001.

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19

Chou, Youn-Min, Robert L. Mason, and John C. Young. "The SPRT Control Chart for Standard Deviation Based on Individual Observations." Quality Technology & Quantitative Management 3, no. 3 (2006): 335–45. http://dx.doi.org/10.1080/16843703.2006.11673119.

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20

Yang, Su-Fen, and Barry C. Arnold. "A Simple Approach for Monitoring Business Service Time Variation." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/238719.

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Control charts are effective tools for signal detection in both manufacturing processes and service processes. Much of the data in service industries comes from processes having nonnormal or unknown distributions. The commonly used Shewhart variable control charts, which depend heavily on the normality assumption, are not appropriately used here. In this paper, we propose a new asymmetric EWMA variance chart (EWMA-AV chart) and an asymmetric EWMA mean chart (EWMA-AM chart) based on two simple statistics to monitor process variance and mean shifts simultaneously. Further, we explore the samplin

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Ali, Sajid, Muhammad Farhan Akram, and Ismail Shah. "Max-EWMA Chart Using Beta and Simplex Distributions for Time and Magnitude Monitoring." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/7306775.

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Control charts are used to detect assignable causes in different manufacturing and nonmanufacturing processes. This study presents a new maximum exponentially weighted moving average (Max-EWMA) chart for joint unit interval time and magnitude monitoring. To this end, beta distribution is considered for time whereas simplex distribution is used for magnitude. Average run length (ARL), standard deviation of run length (SDRL), and different quantiles are used to evaluate the performance of the Max-EWMA chart. A real data example is also included in the study to show the application of the propose

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22

Khan, Imad, Muhammad Noor-ul-Amin, Narjes Turki Khalifa, and Asma Arshad. "EWMA control chart using Bayesian approach under paired ranked set sampling schemes: An application to reliability engineering." AIMS Mathematics 8, no. 9 (2023): 20324–50. http://dx.doi.org/10.3934/math.20231036.

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<abstract> <p>Control charts are widely used to efficiently detect small to moderate shifts and they include exponentially moving average control charts, named memory type control charts. Today, memory type control charts are a significant tool to assure quality standards and monitor manufacturing goods. The proposed study suggests a novel Bayesian exponentially weighted moving average (EWMA) control chart design utilizing various pair ranked set sampling schemes for posterior and posterior predictive distributions given an informative prior. The proposed chart strategy is evaluate

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Mangold, Benedikt, та Jens Konopik. "Introduction of a general class of entropy-based control charts: The Φ-chart". Managerial Economics 25, № 1 (2024): 55. http://dx.doi.org/10.7494/manage.2024.25.1.55.

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We introduce a new class of Shewhart control charts, namely the f-chart. This new class is based on the cumulative paired f-divergence that generalizes both the cumulative (residual) entropy and the differential entropy. The f-chart contains several subclasses; of which one has a special case, the G-chart, which uses Gini’s mean difference as a measure of dispersion. We investigate the performance of three of the subclasses of f-charts in a showcase scenario, comparing its average run length under the Gaussian and several alternative distributions relevant to process control. We find especiall

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Yahaya, Masthura, Sok Li Lim, Adriana Irawati Ibrahim, Wai Chung Yeong, and Michael Boon Chong Khoo. "A VARIABLE SAMPLE SIZE SYNTHETIC CHART FOR THE COEFFICIENT OF VARIATION." South African Journal of Industrial Engineering 33, no. 1 (2022): 16–24. http://dx.doi.org/10.7166/32-4-2545.

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A variable sample size (VSS) synthetic chart to monitor the coefficient of variation is proposed in this paper to improve the performance of the existing synthetic chart. A description of how the chart operates, as well as the formulae for various performance measures (i.e., the average run length (ARL), standard deviation of the run length (SDRL), average sample size (ASS), and expected average run length (EARL)) are proposed. The algorithms that optimise the out-of-control ARL (ARL1) and EARL (EARL1), subject to the constraints in the in-control ARL (ARL0) and ASS (ASS0), are also proposed.

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Kalpesh S. Tailor. "Exponentially Weighted Moving Average (EWMA) Chart Based on Six Delta Initiatives." Mathematical Journal of Interdisciplinary Sciences 6, no. 2 (2018): 127–35. http://dx.doi.org/10.15415/mjis.2018.62010.

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A control chart is a graphical device for representation of the data for knowing the extent of variations from the expected standard. The technique of control chart was suggested by W.A. Shewhart of Bell Telephone Company based on three sigma limits. M. Harry, the engineer of Motorola has introduced the concept of six sigma in 1980. In six sigma initiatives, it is expected to produce 3.4 or less number of defects per million of opportunities. Moderate distribution proposed by Naik and Desai is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters a

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Joghee, Ravichandran. "Control chart for high-quality processes based on Six Sigma quality." International Journal of Quality & Reliability Management 34, no. 1 (2017): 2–17. http://dx.doi.org/10.1108/ijqrm-05-2015-0080.

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Purpose The purpose of this paper is to develop an innovative and quite new Six Sigma quality control (SSQC) chart for the benefit of Six Sigma practitioners. A step-by-step procedure for the construction of the chart is also given. Design/methodology/approach Under the assumption of normality, in this paper, the construction of SSQC chart is proposed in which the population mean and standard deviation are drawn from the process specification from the perspective of Six Sigma quality (SSQ). In this chart, the concept of target range is used to restrict the shift in the process within plus or m

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Ng, Kooi Huat, Kok Haur Ng, and Jeng Young Liew. "Change point detection in process control with robust individuals control chart." ITM Web of Conferences 36 (2021): 01006. http://dx.doi.org/10.1051/itmconf/20213601006.

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It is crucial to realize when a process has changed and to what extent it has changed, then it would certainly ease the task. On occasion that practitioners could determine the time point of the change, they would have a smaller search window to pursue for the special cause. As a result, the special cause can be discovered quicker and the necessary actions to improve quality can be triggered sooner. In this paper, we had demonstrated the use of so-called exploratory data analysis robust modified individuals control chart incorporating the M-scale estimator and had made some comparisons to the

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Riaz, Muhammad, Saddam Akber Abbasi, Muhammad Abid, and Abdulhammed K. Hamzat. "A New HWMA Dispersion Control Chart with an Application to Wind Farm Data." Mathematics 8, no. 12 (2020): 2136. http://dx.doi.org/10.3390/math8122136.

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Recently, a homogeneously weighted moving average (HWMA) chart has been suggested for the efficient detection of small shifts in the process mean. In this study, we have proposed a new one-sided HWMA chart to effectively detect small changes in the process dispersion. The run-length (RL) profiles like the average RL, the standard deviation RL, and the median RL are used as the performance measures. The RL profile comparisons indicate that the proposed chart has a better performance than its existing counterpart’s charts for detecting small shifts in the process dispersion. An application relat

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Zheng, Jiewen. "Python-based Population Forecasting with Standard Deviation Analysis." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 963–68. http://dx.doi.org/10.54097/mmsxy129.

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Changes in the world's population are a constant problem, such as the aging crescent, declining fertility and so on. Having an accurate forecast can chart a better and more useful plan for future development. The accuracy of the python prediction is obtained by computing the standard deviation of the python prediction and the raw data. The code runs within an extremely basic set of read, judge, and compute rules. This data set has been obtained from more than 10, 000 data sets of populations between 1950 and 2021 in different age conditions and countries. Each individual age segment gets a dif

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Albazli, Ahmad O., Muhammad Aslam, and Saeed A. Dobbah. "A Control Chart for Exponentially Distributed Characteristics Using Modified Multiple Dependent State Sampling." Mathematical Problems in Engineering 2020 (December 25, 2020): 1–26. http://dx.doi.org/10.1155/2020/5682587.

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In this paper, a t-control chart based on modified multiple dependent state sampling is proposed for monitoring processes that assume time between events following exponential distribution. The chart has double control limits and employs information from a previous sample and the current sample. The control chart coefficient “constants” are estimated by considering different values of the in-control average run lengths. The detection ability of the proposed control chart is found to be better than that of control charts based on multiple dependent state sampling in terms of average run lengths

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Huang, H. J., and F. L. Chen. "A synthetic control chart for monitoring process dispersion with sample standard deviation." Computers & Industrial Engineering 49, no. 2 (2005): 221–40. http://dx.doi.org/10.1016/j.cie.2004.11.001.

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Faria Sobue, Cassio Eduardo, Felipe Schoemer Jardim, Victor Claudio Bento Camargo, Fabiane Leticia Lizarelli, and Pedro Carlos Oprime. "Unconditional performance of the X¯ chart: Comparison among five standard deviation estimators." Quality and Reliability Engineering International 36, no. 5 (2020): 1808–19. http://dx.doi.org/10.1002/qre.2661.

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Karoon, Kotchaporn, and Yupaporn Areepong. "The Efficiency of the New Extended EWMA Control Chart for Detecting Changes Under an Autoregressive Model and Its Application." Symmetry 17, no. 1 (2025): 104. https://doi.org/10.3390/sym17010104.

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Control charts are frequently used instruments for process quality monitoring. Another name for the NEEWMA control chart is the new extended exponentially weighted moving average (new extended EWMA) control chart. The lower control limit (LCL) and upper control limit (UCL) are equally spaced from the center line, giving it a symmetrical design. Because of its symmetry, the NEEWMA chart is very good at identifying even the tiniest changes in operation by detecting deviations from the target in both upward and downward directions. This study derives an explicit formula for the average run length

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Phantu, Suganya, Yupaporn Areepong, and Saowanit Sukparungsee. "Robustness of Moving Average-Exponential Weighted Moving Average Control Chart with the Light-Tailed Distribution." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 19 (October 22, 2024): 255–66. http://dx.doi.org/10.37394/23203.2024.19.28.

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Control charts are the most significant statistical process control tools for ensuring industrial process reliability and efficiency. This research uses moving average – exponentially weighted moving average (MA-EWMA) control charts to investigate the dispersion characteristics. The control chart performance compares the arithmetic mean of run length (AMRL) and standard deviation of run length (SDRL) profiles of MA-EWMA and synthetic charts. Student's t-distributions are compared for dispersion processes. Finally, we present a case to show how essential control charts are in practice.

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Le Goh, Kai, Lin Wei Teoh, Lin Zhi Chong, Lin Kai Ong, and Laila El-Ghandour. "A study on the performances of the run sum X̄ chart under the gamma process." ITM Web of Conferences 67 (2024): 01002. http://dx.doi.org/10.1051/itmconf/20246701002.

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The run sum (RS) X̄ chart is known as a simple and powerful tool for monitoring the mean of a process. Most developments of the RS X̄ chart assume that the underlying process comes from a normal distribution. However, in practice, many processes tend to follow a non-normal distribution. These non-normal processes affect the performances of control charts under the design of normal distribution. In this paper, we present a detailed analysis on the performances of the RS X̄ chart when the underlying data come from a gamma distribution. By using Monte Carlo simulation approach, the run-length pro

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Rashid, Kawa. "Design Tukey’s Control Chart and mix with CUSUM Control Chart." Journal of Zankoy Sulaimani - Part A 24, no. 1 (2022): 55–66. http://dx.doi.org/10.17656/jzs.10869.

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Statistical process control is a collection of valuable tools for detecting alteration in a process. It has wide application in many areas field and other fields where variation is being monitored. The variation may be a natural cause variation or a particular cause variation. Statistical process control deals with the monitoring process to detect disturbances in the process. These disturbances may be from the process mean or variance. This study proposes efficient charts for detecting early shifts in dispersion parameters by applying the Fast Initial Response feature. We propose and compare t

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Birdawod, Hawkar Q., Dlshad M. Saleh, Dashty I. Jamil, and AbdulRahim K. Rahi. "Adopting Discrete Wavelet Transformation with Mean Time Between Failure to Design a New Quality Control Chart." Cihan University-Erbil Scientific Journal 9, no. 1 (2025): 47–51. https://doi.org/10.24086/cuesj.v9n1y2025.pp47-51.

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This study’s goal was to design a new individual observation chart for quality control by utilizing the discrete wavelet transformation (DWT) approach to calculate the mean time between reliability failures. The new chart’s effectiveness was demonstrated by comparing it to a classical chart. This aspect is crucial in addition to the conclusion that the production process is under control for both charts, with the new chart exhibiting a reduced standard deviation. The study’s main focus was on a new area for device monitoring and defect detection, which involves halting production and eliminati

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Yeo, George SH, Maili Qi, Ruochen Du, et al. "Gender-Specific Reference Charts of Fetal Head Circumference in a Singaporean Population." Annals of the Academy of Medicine, Singapore 46, no. 10 (2017): 367–73. http://dx.doi.org/10.47102/annals-acadmedsg.v46n10p367.

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Introduction: With the global outbreak of Zika virus and its association with microcephaly, an up-to-date fetal head circumference (HC) nomogram is crucial to offer a reference standard in order to make an accurate diagnosis. This study was conducted to revise the local fetal HC nomogram. Materials and Methods: In this retrospective study, ultrasound data was used for construction of the fetal HC nomogram from a total of 6155 pregnancies in the ethnic Chinese population with low risk profile at KK Women’s and Children’s Hospital over a 10-year period. Regression model was fitted to calculate t

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Sunthornwat, Rapin, Saowanit Sukparungsee, and Yupaporn Areepong. "The Development and Evaluation of Homogenously Weighted Moving Average Control Chart based on an Autoregressive Process." HighTech and Innovation Journal 5, no. 1 (2024): 16–35. http://dx.doi.org/10.28991/hij-2024-05-01-02.

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This research aims to investigate a Homogenously Weighted Moving Average (HWMA) control chart for detecting minor and moderate shifts in the process mean. A mathematical model for the explicit formulae of the average run length (ARL) of the HWMA control chart based on the autoregressive (AR) process is presented. The efficacy of the HWMA control chart is evaluated based on the average run length, the standard deviation of run length (SDRL), and the median run length (MRL). As illustrations of the design and implementation of the HWMA control chart, numerical examples are provided. In numerous

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Khan, Imad, Dost Muhammad Khan, Muhammad Noor-ul-Amin, Umair Khalil, Huda M. Alshanbari, and Zubair Ahmad. "Hybrid EWMA Control Chart under Bayesian Approach Using Ranked Set Sampling Schemes with Applications to Hard-Bake Process." Applied Sciences 13, no. 5 (2023): 2837. http://dx.doi.org/10.3390/app13052837.

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A memory-type control chart is an important tool of statistical process control for monitoring small to moderate shifts in the manufacturing process. Using the prior information by the Bayesian approach is helpful in control charts. In this paper, a new hybrid exponentially weighted moving average (HEWMA) control chart is suggested under the Bayesian theory using ranked set sampling (RSS) schemes for posterior and posterior predictive distribution with informative prior and different loss functions (LFs). The extensive Monto Carlo simulation is conducted to evaluate the overall performance of

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Yeong, Wai Chung, Sok Li Lim, Zhi Lin Chong, Michael B. C. Khoo, and Sajal Saha. "A side-sensitive synthetic chart for the multivariate coefficient of variation." PLOS ONE 17, no. 7 (2022): e0270151. http://dx.doi.org/10.1371/journal.pone.0270151.

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Control charts for the coefficient of variations (γ) are receiving increasing attention as it is able to monitor the stability in the ratio of the standard deviation (σ) over the mean (μ), unlike conventional charts that monitor the μ and/or σ separately. A side-sensitive synthetic (SS) chart for monitoring γ was recently developed for univariate processes. The chart outperforms the non-side-sensitive synthetic (NSS) γ chart. However, the SS chart monitoring γ for multivariate processes cannot be found. Thus, a SS chart for multivariate processes is proposed in this paper. A SS chart for multi

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Maqsood, Mediha, Aamir Sanaullah, Yasar Mahmood, Afrah Yahya Al-Rezami, and Manal Z. M. Abdalla. "Efficient control chart-based monitoring of scale parameter for a process with heavy-tailed non-normal distribution." AIMS Mathematics 8, no. 12 (2023): 30075–101. http://dx.doi.org/10.3934/math.20231538.

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<abstract> <p>Statistical process control is a procedure of quality control that is widely used in industrial processes to enable monitoring by using statistical techniques. All production processes are faced with natural and unnatural variations. To maintain the stability of the production process and reduce variation, different tools are used. Control charts are significant tools to monitor a production process. In this article, we design an extended exponentially weighted moving average (EEWMA) chart under the assumption of inverse Maxwell (IM) distribution, an IM EEWMA (IMEEWMA

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Wibawati, Wibawati, Muhammad Ahsan, and Hidayatul Khusna. "Neutrosophic multivariate EWMA control chart." Decision Science Letters 12, no. 4 (2023): 807–16. http://dx.doi.org/10.5267/j.dsl.2023.6.001.

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The MEWMA chart is one of the traditional multivariate charts which are widely employed in inspecting the quality of manufacturing and services. This chart is created through monitoring the small shifts of mean vectors of variable quality characteristics. Often in practice, the measurement of a quality characteristic produces uncertain, incomplete values, so that ambiguous numbers are obtained. In this condition, a neutrosophic-based control chart can overcome the problem resulting from the ambiguous data. The paper’s objective is to construct a new multivariate monitoring scheme based on a ne

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Talordphop, Khanittha, Saowanit Sukparungsee, and Yupaporn Areepong. "Performance of new nonparametric Tukey modified exponentially weighted moving average—Moving average control chart." PLOS ONE 17, no. 9 (2022): e0275260. http://dx.doi.org/10.1371/journal.pone.0275260.

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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 e

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Sivakumaran, PK, and T. Kanneswari. "Development of six sigma-based control chart for standard deviation under robust measure." International Journal of Statistics and Applied Mathematics 9, no. 6 (2024): 235–42. https://doi.org/10.22271/maths.2024.v9.i6c.1926.

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Maravelakis, Petros E., and Philippe Castagliola. "An EWMA chart for monitoring the process standard deviation when parameters are estimated." Computational Statistics & Data Analysis 53, no. 7 (2009): 2653–64. http://dx.doi.org/10.1016/j.csda.2009.01.004.

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Abujiya, Mu'azu Ramat, Muhammad Riaz, and Muhammad Hisyam Lee. "A New Combined Shewhart-Cumulative Sum S Chart for Monitoring Process Standard Deviation." Quality and Reliability Engineering International 32, no. 3 (2015): 1149–65. http://dx.doi.org/10.1002/qre.1822.

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Oliveira, Danilo Tedesco de, Leonardo Bernache, Luan Pereira de Oliveira, Murilo Aparecido Voltarelli, and Rouverson Pereira da Silva. "A New Approach to Statistical Process Control: Identification of Outliers in Yield Maps." Journal of Agricultural Science 10, no. 8 (2018): 334. http://dx.doi.org/10.5539/jas.v10n8p334.

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The tools of precision agriculture are of utmost importance in the Brazilian agribusiness, enabling increases in yields and reducing production costs. The use of harvest monitoring systems makes it possible due the possibility to identify pontual problems in an area, however, it becomes necessary to be working properly so it does not acquire incorrect information. Therefore, the purpose with this study was to propose a new approach to identify discrepant points in harvesting maps using statistical process control, as well as to define the best multiple of the standard deviation to identificate

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Riaza, Muhammad, and Saddam Akber Abbasib. "Nonparametric Double EWMA Control Chart for Process Monitoring." Revista Colombiana de Estadística 39, no. 2 (2016): 167. http://dx.doi.org/10.15446/rce.v39n2.58914.

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<p>In monitoring process parameters, we assume normality of the quality characteristic of interest, which is an ideal assumption. In many practical sit- uations, we may not know the distributional behavior of the data, and hence, the need arises use nonparametric techniques. In this study, a nonparametric double EWMA control chart, namely the NPDEWMA chart, is proposed to ensure efficient monitoring of the location parameter. The performance of the proposed chart is evaluated in terms of different run length properties, such as average, standard deviation and percentiles. The proposed sc

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Kalpesh S Tailor. "Performance of Moving Average (MA) Chart Under Three Delta Control Limits and Six Delta Initiatives." Mathematical Journal of Interdisciplinary Sciences 7, no. 2 (2019): 157–60. http://dx.doi.org/10.15415/mjis.2019.72020.

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An SQC chart is a graphical tool for representation of the data for knowing the extent of variations from the expected standard. This technique was first suggested by W.A. Shewhart of Bell Telephone Company based on 3σ limits. M. Harry, the engineer of Motorola has introduced the concept of six sigma in 1980. In 6σ limits, it is presumed to attain 3.4 or less number of defects per million of opportunities. Naik V.D and Desai J.M proposed an alternative of normal distribution, which is named as moderate distribution. The parameters of this distribution are mean and mean deviation. Naik V.D and

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