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Journal articles on the topic 'Mean chart'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Huay Woon, You. "A Comparative Analysis of Control Charts for Monitoring Process Mean." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 2616–22. http://dx.doi.org/10.17762/turcomat.v12i3.1263.

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Control charts serve as an effective tool for controlling and monitoring process quality in industries of production and service. The Shewhart chart is the first control chart that was used to detect large mean shifts in a process. Since then, to increase the Shewhart chart’s sensitivity, synthetic type control charts, such as synthetic control chart, side sensitive group runs (SSGR) control chart, have been proposed. SSGR chart ismore efficient compared to the Shewhart chart and synthetic chart,primarily due to the side sensitive feature in SSGR chart. Meanwhile, exponentially weighted moving

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Costa, Antônio Fernando Branco, and Marcela Aparecida Guerreiro Machado. "Monitoring the mean vector and the covariance matrix of multivariate processes with sample means and sample ranges." Production 21, no. 2 (2011): 197–208. http://dx.doi.org/10.1590/s0103-65132011005000029.

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The joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and R charts and the joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and S² charts are the most common charts used for monitoring the process mean and dispersion. With the usual sample sizes of 4 and 5, the joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and R charts are slightly inferior to the joint <img src="/img/revistas/prod/2011nahead/aop_t6_0002_0329.jpg" /> and S² charts in terms of efficiency in detecting process shifts. In this article, we s

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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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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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Su-Fen, Yang, Tsai Wen-Chi, Huang Tzee-Ming, Yang Chi-Chin, and Cheng Smiley. "Monitoring process mean with a new EWMA control chart." Production 21, no. 2 (2011): 217–22. http://dx.doi.org/10.1590/s0103-65132011005000026.

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In practice, sometimes the process data did not come from a known population distribution. So the commonly used Shewhart variables control charts are not suitable since their performance could not be properly evaluated. In this paper, we propose a new EWMA Control Chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. The sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. Furthermore, an Arcsine EWMA Chart is proposed since the average run lengths o

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YEH, ARTHUR B., and DENNIS K. J. LIN. "A NEW VARIABLES CONTROL CHART FOR SIMULTANEOUSLY MONITORING MULTIVARIATE PROCESS MEAN AND VARIABILITY." International Journal of Reliability, Quality and Safety Engineering 09, no. 01 (2002): 41–59. http://dx.doi.org/10.1142/s0218539302000652.

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In this paper, we propose a new variables control chart, called the box-chart, to simultaneously monitor, on a single chart, the process mean and process variability for multivariate processes. The box-chart uses a probability integral transformation to obtain two independently and identically distributed uniform distributions. Therefore, a box-shaped (thus the name), two-dimensional control chart can be constructed. We discuss in detail on how to construct the box-chart. The proposed chart is applied to two real-life examples. The performance of the box-chart is also compared to that of the t

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Xia, Beixin, Zheng Jian, Lei Liu, and Long Li. "An effective multivariate control chart for detecting small mean shifts using support vector data description." Advances in Mechanical Engineering 10, no. 11 (2018): 168781401881062. http://dx.doi.org/10.1177/1687814018810625.

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Conventional multivariate cumulative sum control charts are more sensitive to small shifts than [Formula: see text] control charts, but they cannot get the knowledge of manufacturing process through the learning of in-control data due to the characteristics of their own structures. To address this issue, a modified multivariate cumulative sum control chart based on support vector data description for multivariate statistical process control is proposed in this article, which is named [Formula: see text] control chart. The proposed control chart will have both advantages of the multivariate cum

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Umar, Adamu A., Michael BC Khoo, Sajal Saha, and Abdul Haq. "A combined variable sampling interval and double sampling control chart with auxiliary information for the process mean." Transactions of the Institute of Measurement and Control 42, no. 6 (2019): 1151–65. http://dx.doi.org/10.1177/0142331219885525.

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In recent years, the suitable use of auxiliary information technique in control charts has shown an improved run length performance compared to control charts that do not have this feature. This article proposes a combined variable sampling interval (VSI) and double sampling (DS) chart using the auxiliary information (AI) technique (called VSIDS-AI chart, hereafter). The plotting-statistic of the VSIDS-AI chart requires information from both the study and auxiliary variables to efficiently detect process mean shifts. The charting statistics, optimal design and performance assessment of the VSI

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Zaman, Bisma, Syed Muhammad Muslim Raza, Javed Iqbal, Naima Shehzadi, Muhammad Moeen Butt, and Muhammad Riaz. "Efficient control charting methodology based on Distance Weighted Mean for normal distribution." Natural and Applied Sciences International Journal (NASIJ) 4, no. 1 (2023): 1–16. http://dx.doi.org/10.47264/idea.nasij/4.1.1.

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This research suggests a Distance Weighted Mean (DWM) based control chart under normal distribution implementing Simple Random Sampling (SRS). The control limits are calculated using the quantile point method. The control chart's performance is assessed using the Average Run Length (ARL) statistic. The numerical findings are illustrated using samples of sizes 3 and 5. The ARL1 values are determined using Monte Carlo Simulation for increasing and decreasing shifts in the location parameter ranging from 5% to 30%. Using the ARL1 measurement, the proposed DWM control charts are compared to the ex

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KHOO, MICHAEL B. C., ZHANG WU, and ABDU M. A. ATTA. "A SYNTHETIC CONTROL CHART FOR MONITORING THE PROCESS MEAN OF SKEWED POPULATIONS BASED ON THE WEIGHTED VARIANCE METHOD." International Journal of Reliability, Quality and Safety Engineering 15, no. 03 (2008): 217–45. http://dx.doi.org/10.1142/s0218539308003052.

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A synthetic control chart for detecting shifts in the process mean integrates the Shewhart [Formula: see text] chart and the conforming run length chart. It is known to outperform the Shewhart [Formula: see text] chart for all magnitudes of shifts and is also superior to the exponentially weighted moving average chart and the joint [Formula: see text]-exponentially weighted moving average charts for shifts of greater than 0.8σ in the mean. A synthetic chart for the mean assumes that the underlying process follows a normal distribution. In many real situations, the normality assumption may not

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Aldosari, Mansour Sattam, Muhammad Aslam, Chi-Hyuck Jun, and Khushnoor Khan. "A New Control Chart for Monitoring the Process Mean Using Successive Sampling and Multiple Dependent State Repetitive Sampling." Technologies 6, no. 3 (2018): 70. http://dx.doi.org/10.3390/technologies6030070.

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In this paper, a new control chart scheme has been developed for monitoring the production process mean using successive sampling over two occasions. The proposed chart reduces to three different existing control charts under different assumptions and is compared with these three existing control charts for monitoring the process average. It has been observed that the proposed control chart performs better than the other existing control charts in terms of average run length (ARL). A simulation study using an artificial data set was included for demonstrating the process shift detection power

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Machado, Marcela A. G., Antonio F. B. Costa, and Fernando A. E. Claro. "Monitoring bivariate process." Pesquisa Operacional 29, no. 3 (2009): 547–62. http://dx.doi.org/10.1590/s0101-74382009000300005.

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The T² chart and the generalized variance |S| chart are the usual tools for monitoring the mean vector and the covariance matrix of multivariate processes. The main drawback of these charts is the difficulty to obtain and to interpret the values of their monitoring statistics. In this paper, we study control charts for monitoring bivariate processes that only requires the computation of sample means (the ZMAX chart) for monitoring the mean vector, sample variances (the VMAX chart) for monitoring the covariance matrix, or both sample means and sample variances (the MCMAX chart) in the case of t

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Susetyo, Budi, Anwar Fitrianto, and Lai Ming Choon. "NEW APPROACH FOR DETECTING SLIGHT SHIFT ON PROCESS MEAN IN CHIP RESISTOR PRODUCTION." Journal of Southwest Jiaotong University 56, no. 5 (2021): 59–66. http://dx.doi.org/10.35741/issn.0258-2724.56.5.6.

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This article highlights an alternative approach to identify a slight shift of the process mean for resistor production. Commonly, the industries use exponentially weighted moving average (EWMA) or classic I-MR charts for this kind of product. The parametric control chart consists of few underlying assumptions, especially observations that come from a normal distribution. A misleading conclusion was mainly made when non-normal distributed data were analyzed using a parametric control chart. A chip resistor manufacturing company provided the data for the study for future quality monitoring purpo

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Arslan, Muhammad, Muhammad Azeem Ashraf, Syed Masroor Anwar, Zahid Rasheed, Xuelong Hu, and Saddam Akbar Abbasi. "Novel Mixed EWMA Dual-Crosier CUSUM Mean Charts without and with Auxiliary Information." Mathematical Problems in Engineering 2022 (March 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/1362193.

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The classical cumulative SUM (CUSUM) chart is commonly used to monitor a particular size of the mean shift. In many real processes, it is assumed that the shift level varies within a range, and the exact level of the shift size is mostly unknown. For detecting a range of shift size, the dual-CUSUM (DC) and dual-Crosier CUSUM (DCC) charts are used to provide better detection ability as compared to the CUSUM and Crosier CUSUM (CC) charts, respectively. This paper introduces a new mixed exponentially weighted moving average (EWMA)-DCC (EDCC) chart to monitor process mean. In addition, AIB-based E

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Ng, Jing Wen, Voon Hee Wong, and Sook Theng Pang. "A synthetic exponentially weighted moving average control chart to monitor process median based on ranked set sampling." ITM Web of Conferences 36 (2021): 01002. http://dx.doi.org/10.1051/itmconf/20213601002.

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Exponentially Weighted Moving Average (EWMA) control charts yield insights into data in a way more comprehensible to the practitioners and researchers because of its capability in discovering small to moderate process mean shifts. EWMA control chart is incorporated with conforming run length (CRL) chart, named synthetic EWMA chart, to enhance the performance of the chart in detecting the out-of-control signal. Synthetic EWMA chart based on ranked set sampling (RSS) for monitoring process mean has been proposed as it advanced the detection of chart over a series of mean shifts. With the situati

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Pervaiz, Hafiz Zain, Syed Muhammad Muslim Raza, Muhammad Moeen Butt, Saira Sharif, and Muhammad Haider. "A New Hybrid Exponentially Weighted Moving Average control chart using Mixture Ratio Estimator of Mean." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 2 (2021): 45–57. http://dx.doi.org/10.53560/ppasa(58-2)606.

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In this paper, we propose a Hybrid Exponentially Weighted Moving Average (HEWMA) control chart based on a mixture ratio estimator of mean using a single auxiliary variable and a single auxiliary attribute (Moeen et al., [1]). We call it as Z- HEWMA control chart. The proposed control chart performance is evaluated using outof- control-Average Run Length (ARL1). The control limits of the proposed chart is based on estimator, its mean square errors. A simulated example is used to compare the proposed Z-HEWMA, traditional/simple EWMA chart and CUSUM control chart. From this study the fact is reve

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HUMAN, S. W., and S. CHAKRABORTI. "A UNIFIED APPROACH FOR SHEWHART-TYPE PHASE I CONTROL CHARTS FOR THE MEAN." International Journal of Reliability, Quality and Safety Engineering 17, no. 03 (2010): 199–208. http://dx.doi.org/10.1142/s0218539310003755.

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The false alarm probability (FAP) is the metric typically used to design and evaluate the performance of Phase I control charts. It is shown that in situations where the exact or the asymptotic joint p.d.f. of the standardized charting statistics follows a singular standard multivariate normal distribution with a common negative correlation, the FAP of some Shewhart-type Phase I charts for the mean can be expressed as a multiple integral of the joint p.d.f. Hence the required charting constants can be calculated (and the control chart can be implemented) by evaluating this integral. A table wi

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Chong, Nger Ling, Michael Boon Chong Khoo, Zhi Lin Chong, and Wei Lin Teoh. "A study on the run length properties of the side sensitive group runs double sampling (SSGRDS) control chart." MATEC Web of Conferences 192 (2018): 01005. http://dx.doi.org/10.1051/matecconf/201819201005.

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The side sensitive group runs double sampling (SSGRDS) chart incorporates the control charting concepts of the side sensitive group runs (SSGR) and double sampling (DS) charts. The SSGRDS chart which combines the efficiency of its basic charts is an effective approach to increase the speed of mean shift detection. The performance of the SSGRDS chart, based on the average number of observations to signal (ANOS), median number of observations to signal (MNOS) and percentiles of the number of observations to signal (PNOS) is investigated in this paper. Based on the results obtained, it is found t

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Shamsuzzaman, Mohammad. "Optimization Design of 2-EWMA Control Chart Based on Random Process Shift." Applied Mechanics and Materials 465-466 (December 2013): 1185–90. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1185.

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The exponentially weighted moving average (EWMA) control charts are widely used for detecting process shifts of small and moderate sizes in Statistical Process Control (SPC).This article presents an algorithm for the optimization design of a multi-EWMA scheme comprising two EWMA control charts (known as 2-EWMA chart) considering random process shifts in mean. The random process shifts in mean is characterized by a Rayleigh distribution. The design algorithm optimizes the charting parameters of the 2-EWMA chart based on loss function. Comparative study shows that the optimal 2-EWMA chart outper

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Yang, Ming Jie, and Xue Min Zi. "The Comparison among Three Control Charts for Monitoring the Auto Correlated Processes." Applied Mechanics and Materials 490-491 (January 2014): 1579–83. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1579.

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We compare the ARL of three charts for monitoring the mean shifts of the first-order auto regressive model to choose a proper control chart. Simulation results show that the REWMA chart has a large superior to the EWMA and T2 the chart when -1<Ø<0, but when Ø>0, the chart is better than the other two charts.

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Fernanda Rifki, Kevin Agung, Muhammad Ahsan, and Muhammad Mashuri. "SIMULATION STUDIES PERFORMANCE OF EWMA-MAX MCHART BASED ON SYNTHETIC DATA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 19, no. 2 (2025): 1353–64. https://doi.org/10.30598/barekengvol19iss2pp1353-1364.

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Quality control has an important role in the manufacturing process. One of the statistical tools used in quality control is Statistical Process Control (SPC). The SPC product is a control chart. A control chart is a graphical tool used to determine if a process is under statistical quality control, helping to identify issues and drive quality improvements. Control charts are usually used to control variables or attribute data quality. Commonly used variable data is data with mean and variability characteristics. Various types of control charts are control charts for mean, control charts for va

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Khan, Muhammad Zahir, Muhammad Farid Khan, Muhammad Aslam, Seyed Taghi Akhavan Niaki, and Abdur Razzaque Mughal. "A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean." Information 9, no. 12 (2018): 312. http://dx.doi.org/10.3390/info9120312.

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Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and lingu

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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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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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Zhang, Lingyun, and Gemai Chen. "An Extended EWMA Mean Chart." Quality Technology & Quantitative Management 2, no. 1 (2005): 39–52. http://dx.doi.org/10.1080/16843703.2005.11673088.

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Abbasi, Saddam Akber. "Poisson progressive mean control chart." Quality and Reliability Engineering International 33, no. 8 (2017): 1855–59. http://dx.doi.org/10.1002/qre.2149.

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Hussain, Shahid, Sun Mei, Muhammad Riaz, and Saddam Akber Abbasi. "On Phase-I Monitoring of Process Location Parameter with Auxiliary Information-Based Median Control Charts." Mathematics 8, no. 5 (2020): 706. http://dx.doi.org/10.3390/math8050706.

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A control chart is often used to monitor the industrial or services processes to improve the quality of the products. Mostly, the monitoring of location parameters, both in Phase I and Phase II, is done using a mean control chart with the assumption that the process is free from outliers or the estimators are correctly estimated from in-control samples. Generally, there are question marks about such kind of narratives. The performance of the mean chart is highly affected in the presence of outliers. Therefore, the median chart is an attractive alternative to the mean chart in this situation. T

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Chen, Jen-Hsiang, and Shin-Li Lu. "A New Sum of Squares Exponentially Weighted Moving Average Control Chart Using Auxiliary Information." Symmetry 12, no. 11 (2020): 1888. http://dx.doi.org/10.3390/sym12111888.

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The concept of control charts is based on mathematics and statistics to process forecast; which applications are widely used in industrial management. The sum of squares exponentially weighted moving average (SSEWMA) chart is a well-known tool for effectively monitoring both the increase and decrease in the process mean and/or variability. In this paper, we propose a novel SSEWMA chart using auxiliary information, called the AIB-SSEWMA chart, for jointly monitoring the process mean and/or variability. With our proposed chart, the attempt is to enhance the performance of the classical SSEWMA ch

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Gani, Walid, and Mohamed Limam. "A One-Class Classification-Based Control Chart Using the K-Means Data Description Algorithm." Journal of Quality and Reliability Engineering 2014 (June 9, 2014): 1–9. http://dx.doi.org/10.1155/2014/239861.

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This paper aims to enlarge the family of one-class classification-based control charts, referred to as OC-charts, and extend their applications. We propose a new OC-chart using the K-means data description (KMDD) algorithm, referred to as KM-chart. The proposed KM-chart gives the minimum closed spherical boundary around the in-control process data. It measures the distance between the center of KMDD-based sphere and the new incoming sample to be monitored. Any sample having a distance greater than the radius of KMDD-based sphere is considered as an out-of-control sample. Phase I and II analysi

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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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Das, Nandini. "Multivariate control chart for controlling variability – A comparative study." International Journal of Engineering, Science and Technology 16, no. 1 (2023): 35–43. http://dx.doi.org/10.4314/ijest.v16i1.4.

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Control chart is an important technique for statistical process control. In most of the practical situations data are collected on more than one characteristic under study. Hence multivariate control chart has drawn the attention of quality control practitioners. For successful implementation of multivariate control chart, two different charts are to be constructed. One is for controlling mean and another is for controlling variance-covariance matrix or ∑ matrix. For effective implementation of control chart for mean, one need to establish that variability is under control. Hence control chart

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Mohd Noor, Nor Fashihah, Ayu Abdul-Rahman, and Abdu Mohammed Ali Atta. "THE PERFORMANCES OF MIXED EWMA-CUSUM CONTROL CHARTS BASED ON MEDIAN-BASED ESTIMATORS UNDER NON-NORMALITY." Jurnal Teknologi 86, no. 1 (2023): 135–43. http://dx.doi.org/10.11113/jurnalteknologi.v86.20450.

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Exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts have been regularly used to monitor small process mean shifts. More recently, a mixture of EWMA and CUSUM charts known as mixed EWMA-CUSUM (MEC) control chart has been introduced for better small shift detection. However, like its predecessor, the MEC chart requires the normality assumption to ensure optimal performances. In the presence of outliers, which is the cause of non-normality, the parameters of the chart may be overestimated, leading to an unreliable monitoring process. To mitigate this problem, this paper

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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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Park, Hyo Il. "Combined Charts for the Mean and Variance." Advanced Materials Research 909 (March 2014): 352–55. http://dx.doi.org/10.4028/www.scientific.net/amr.909.352.

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In this study, we consider to propose combined control charts for the process mean and variance to control simultaneously. First of all, we review the existing combined control charts and then proposed new control charts based on the union-intersection test for the jointly likelihood ratio statistics. Also we adopt the Liptak combining function for another combined control chart. For those combined control charts, we use the fact that a series of the hypothesis tests is equivalent to the maintenance of control charts. In order to combine the two individual tests for the respective mean and var

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Yang, Chung Ming, Su Fen Yang, and Jeng Sheng Lin. "A New EWMA Loss Control Chart with Adaptive Control Scheme." Applied Mechanics and Materials 631-632 (September 2014): 12–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.12.

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A single chart, instead of and R charts or and S charts, to simultaneously monitor the process mean and variability would reduce the required time and effort. A number of studies have attempted to find such charts. Moreover, a number of studies demonstrated that the adaptive control charts may detect process shifts faster than the fixed control charts. This paper proposes the EWMA loss chart with variable sample sizes and sampling intervals (VSSI) to effectively monitor the difference of process measurements and target. An example is used to illustrate the application and performance of the pr

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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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SHEU, SHEY-HUEI, and SHIN-LI LU. "MONITORING AUTOCORRELATED PROCESS MEAN AND VARIANCE USING A GWMA CHART BASED ON RESIDUALS." Asia-Pacific Journal of Operational Research 25, no. 06 (2008): 781–92. http://dx.doi.org/10.1142/s0217595908002012.

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This investigation elucidates the feasibility of monitoring a process for which observational data are largely autocorrelated. Special causes typically affect not only the process mean but also the process variance. The EWMA control chart has recently been developed and adopted to detect small shifts in the process mean and/or variance. This work extends the EWMA control chart, called the generally weighted moving average (GWMA) control chart, to monitor a process in which the observations can be regarded as a first-order autoregressive process with a random error. The EWMA and GWMA control ch

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Talordphop, Khanittha, Yupaporn Areepong, and Saowanit Sukparungsee. "Design and Analysis of Extended Exponentially Weighted Moving Average Signed-Rank Control Charts for Monitoring the Process Mean." Mathematics 11, no. 21 (2023): 4482. http://dx.doi.org/10.3390/math11214482.

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In the real world, a nonparametric control chart is a powerful substitute for enhancing outcome quality, although the fundamental procedure characteristic often fails to match the distribution assumptions. This study aims to construct and evaluate an extended exponentially weighted moving average control chart based on signed-rank statistics (EEWMA-SR) for recognizing changes in procedures. According to the study, the proposed chart proves more potent recognition of shifts in the process mean, predominantly small shifts, than the other control charts by the average run length in Monte Carlo si

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Alphonce, Filbert, Gadde Srinivasa Rao, and Emmanuel Deogratias. "Students’ performance evaluation for secondary school in Mbulu district using mean and dispersion charts grade and fixed grading method." Biometrics & Biostatistics International Journal 11, no. 4 (2022): 152–57. http://dx.doi.org/10.15406/bbij.2022.11.00370.

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This article focuses on comparing the evaluation of students’ performance using the Mean and dispersion chart grade and fixed grading method. This quantitative study was conducted in Mbulu district council. The assessment of students’ performance is one of the challenges that face assessors in secondary schools. This quantitative study investigated students’ performance by comparing their performance using statistical quality control charts grade and fixed grading method. The study used secondary data collected from 240 sampled documents obtained from academic students’ progressive reports fro

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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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N, Kara-Junior. "Good Outcome on Snellen Chart Does Not Mean Good Vision." Open Access Journal of Ophthalmology 8, no. 1 (2023): 1–2. http://dx.doi.org/10.23880/oajo-16000268.

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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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Haq, Abdul, and Lubna Bibi. "A new dual CUSUM mean chart." Quality and Reliability Engineering International 35, no. 4 (2019): 1245–62. http://dx.doi.org/10.1002/qre.2457.

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Naveed, Muhammad, Muhamma Azam, Nasrullah Khan, and Muhammad Aslam. "Design of a Control Chart Using Extended EWMA Statistic." Technologies 6, no. 4 (2018): 108. http://dx.doi.org/10.3390/technologies6040108.

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In the present paper, we propose a control chart based on extended exponentially weighted moving average (EEWMA) statistic to detect a quick shift in the mean. The mean and variance expression of the proposed EEWMA statistic are derived. The proposed EEWMA statistic is unbiased and simulation results show a smaller variance as compared to the traditional EWMA. The performance of the proposed control chart with the existing chart based on the EWMA statistic is evaluated in terms of average run length (ARL). Various tables were constructed for different values of parameters. The comparison of th

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Huang, Wei-Heng, and Arthur B. Yeh. "A Nonparametric Phase I Control Chart for Monitoring the Process Variability with Individual Observations Based on Empirical Likelihood Ratio." International Journal of Reliability, Quality and Safety Engineering 25, no. 03 (2018): 1850015. http://dx.doi.org/10.1142/s0218539318500158.

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Among the statistical process control (SPC) techniques, the control chart has been proven to be effective in process monitoring. The Shewhart chart is one of the most commonly used control charts for monitoring the process mean and variability based on the assumption that the distribution of the quality characteristic is normal. However, in practice, many quality characteristics are not normally distributed. Most of the existing nonparametric control charts are designed for Phase II monitoring. Little has been done in developing the nonparametric Phase I control charts especially for individua

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Wang, Tao, Chen Chen, Ding-Wei Liu, and Jing-Lin Yi. "A comparison of visual acuity measured by ETDRS chart and Standard Logarithmic Visual Acuity chart among outpatients." International Journal of Ophthalmology 14, no. 4 (2021): 536–40. http://dx.doi.org/10.18240/ijo.2021.04.09.

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AIM: To compare the results of visual acuity (VA) measured by Early Treatment Diabetic Retinopathy Study (ETDRS) chart, 5 m Standard Logarithm Visual Acuity (5SL) chart, and 2.5 m Standard Logarithm Visual Acuity (2.5SL) chart in outpatients of age 12-80y. METHODS: Each patient (totally 2000 outpatients) had both eyes tested with ETDRS chart at 4 m, 5SL chart at 5 m, and 2.5SL chart at 2.5 m in random order. The VA values of outpatients were categorized by ages. VA values were expressed by logMAR recording method. RESULTS: The mean VA results of ETDRS charts, 5SL, and 2.5SL chart were 0.52±0.2

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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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González Borja, Joaquín, Haider Montes Masmela, and Oscar Andrés Lugo Capera. "Comparando la Eficiencia de los Gráficos T2 de Hotelling y de Clasificación por Rangos, Utilizando Diversos Estimadores de Localización y Dispersión." Entre Ciencia e Ingeniería 6, no. 11 (2012): 164–82. https://doi.org/10.31908/19098367.690.

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Quality is defined simultaneously by a set of correlated variables for many products or services. To monitor a multivariate process, the classic control chart T2 (Hotelling (1947)) is often used. This chart is constructed under the assumption of normality of the observations and estimates of usual location and dispersion. It is well known that this chart is very sensitive to the presence of outliers in the historical data set, causing the masking effect and therefore several proposals to construct charts T2 with alternative estimators that have become more powerful and robust charts in the pre

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Abdelwanis, Naji, Asma Alkshir, Eman Al-Amami, Sondos AlWarfali, and Abdelaziz Badi. "Implementation of Statistical Control Processes in Benghazi's Food Industry." Scientific Journal of University of Benghazi 37, no. 2 (2024): 115–20. https://doi.org/10.37376/sjuob.v37i2.7134.

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Quality control is a crucial aspect of any organization aiming to reach a high-quality level by providing products or services that satisfy customer needs. Control charts are among the most effective tools used to detect the occurrence of defects in the manufacturing process so they can be fixed before being delivered to the customers. This paper aims to assess the filling machine's performance in the Al-WAHA factory. In this investigation, a control chart and process capacity were implemented. Comprehensive details on the control chart and process capacity are discussed, along with an overvie

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Moelyo, Annang Giri, Dewinda Candrarukmi, and Ulfa Puspita Rachma. "Using the National Indonesian Growth Chart to assess short stature and obesity in urban schoolchildren in Surakarta, Indonesia: comparisons to the WHO 2007 and CDC 2000 Growth Charts." Paediatrica Indonesiana 62, no. 3 (2022): 180–5. http://dx.doi.org/10.14238/pi62.3.2022.180-5.

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Background The National Indonesian Growth Chart (NIGC) is a new growth chart based on Indonesian population data. To date, the CDC 2000 or WHO 2007 charts have been widely used in Indonesia to assess the growth of 5-to-18-year-old children. Use of these reference charts may lead to inaccurate conclusions about children’s nutritional status, particularly when diagnosing short stature or obesity.  Objective To compare assessments of short stature and obesity in Indonesian urban schoolchildren and adolescents based on CDC, WHO, and NIGC reference charts. Methods Pooled anthropometric

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