Journal articles: 'Chart of control'

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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 proposed control chart in detecting the changes in the difference of the process measurements and target. Numerical analyses demonstrated that the VSSI EWMA loss chart outperforms the fixed sampling interval EWMA average loss chart and the Shewhart joint and S charts. Therefore, the VSSI EWMA loss chart is recommended.

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Ramirez-Mendez, Esmeralda, and Mario Cantu-Sifuentes. "Multiatributte Double Sampling Control Chart." Industrial and Systems Engineering Review 2, no. 1 (July 8, 2014): 42–51. http://dx.doi.org/10.37266/iser.2014v2i1.pp42-51.

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In recent years, multiattribute control charts have received an increasing attention. These charts are able to monitor two or more attributes in the same chart. In addition, there are many applications of multiatributte control charts in a wide variety of manufacturing processes and services. In this article, a multiattribute double sampling (DS D2) control chart is proposed. Double sampling is a methodology used to improve the efficiency of a control chart to detect quality issues without increase the sampling. Results of comparative studies via simulation indicate that the proposed control chart significantly outperforms in most of the supposed sceneries, in terms of the Average Run Length.

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Rashid, Kawa M. Jamal, and Suzan S. Haydar. "Construction of control charts by using Fuzzy Multinomial -FM and EWMA Chart “Comparative study"." Journal of Zankoy Sulaimani - Part A 16, no. 3 (July 3, 2014): 21–26. http://dx.doi.org/10.17656/jzs.10300.

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Elevli, Sermin, Nevin Uzgören, Deniz Bingöl, and Birol Elevli. "Drinking water quality control: control charts for turbidity and pH." Journal of Water, Sanitation and Hygiene for Development 6, no. 4 (September 26, 2016): 511–18. http://dx.doi.org/10.2166/washdev.2016.016.

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Water treatment processes are required to be in statistical control and capable of meeting drinking water specifications. Control charts are used to monitor the stability of quality parameters by distinguishing the in-control and out-of-control states. The basic assumption in standard applications of control charts is that observed data from the process are independent and identically distributed. However, the independence assumption is often violated in chemical processes such as water treatment. Autocorrelation, a measure of dependency, is a correlation between members of a series arranged in time. The residuals obtained from an autoregressive integrated moving averages (ARIMA) time series model plotted on a standard control chart is used to overcome the misleading of standard control charts in the case of autocorrelation. In this study, a special cause control (SCC) chart, also called a chart of residuals from the fitted ARIMA model, has been used for turbidity and pH data from a drinking water treatment plant in Samsun, Turkey. ARIMA (3,1,0) for turbidity and ARIMA (1,1,1) for pH were determined as the best time series models to remove autocorrelation. The results showed that the SCC chart is more appropriate for autocorrelated data to evaluate the stability of the water treatment process, since it provides a higher probability of coverage than an individual control chart.

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Liu, Jian, Kai Yun Yang, and Wei Wen. "A modified MEWMA control chart: PEWMA control chart." International Journal of Management Concepts and Philosophy 10, no. 2 (2017): 184. http://dx.doi.org/10.1504/ijmcp.2017.084052.

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Huay Woon, You. "A Comparative Analysis of Control Charts for Monitoring Process Mean." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 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 average (EWMA) chart isoften used to detect small process changes. In practice, the evaluation of a control chart’s performance is vital. Nevertheless, the cost of implementing a control chart is an important factor that influences the choice of a control chart. The cost of repairs, sampling, nonconforming products from a failure in detecting out-of-control status, and investigating false alarms, can be significantly high. Therefore, the aim of this paper is to compare the implementation cost of synthetic, SSGR and EWMA charts, so that quality practitioners can identify the most cost-effective chart to implement. Here, the cost function was adopted to compute the implementation cost of the control chart. According to the findings, quality practitioners are recommended to adopt the SSGR chart,since it is more economical compared to the synthetic chart. However, the cost to implement anEWMA chart is higher than the synthetic and SSGR charts. In light of this, this study allows for quality practitioners to have a better idea on the selection of the control chart to implement, with respect to its cost.

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Gani, Walid, and Mohamed Limam. "On the Use of the K-Chart for Phase II Monitoring of Simple Linear Profiles." Journal of Quality and Reliability Engineering 2013 (June 5, 2013): 1–8. http://dx.doi.org/10.1155/2013/705450.

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Control charts for monitoring linear profiles are used to control quality processes which are characterized by a relationship between a response variable and one or more explanatory variables. In the literature, the majority of control charts deal with phase II analysis of linear profiles, where the objective is to assess the performance of control charts in detecting shifts in the parameters of linear profiles. Recently, the kernel distance-based multivariate control chart, also known as the K-chart, has received much attention as a promising nonparametric control chart with high sensitivity to small shifts in the process. Despite its numerous advantages, no work has proposed the use of the K-chart for monitoring simple linear profiles and that serves the motivation for this paper. This paper proposes the use of the K-chart for monitoring simple linear profiles. A benchmark example is used to show the construction methodology of the K-chart for simultaneously monitoring the slope and intercept of linear profile. In addition, performance of the K-chart in detecting out-of-control profiles is assessed and compared with traditional control charts. Results demonstrate that the K-chart performs better than the T2 control chart, EWMA control chart, and R-chart under small shift in the slope.

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Machado, Marcela A. G., and Antonio F. B. Costa. "The use of principal components and univariate charts to control multivariate processes." Pesquisa Operacional 28, no. 1 (April 2008): 173–96. http://dx.doi.org/10.1590/s0101-74382008000100010.

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In this article, we evaluate the performance of the T² chart based on the principal components (PC X chart) and the simultaneous univariate control charts based on the original variables (SU charts) or based on the principal components (SUPC charts). The main reason to consider the PC chart lies on the dimensionality reduction. However, depending on the disturbance and on the way the original variables are related, the chart is very slow in signaling, except when all variables are negatively correlated and the principal component is wisely selected. Comparing the SU , the SUPC and the T² charts we conclude that the SU X charts (SUPC charts) have a better overall performance when the variables are positively (negatively) correlated. We also develop the expression to obtain the power of two S² charts designed for monitoring the covariance matrix. These joint S² charts are, in the majority of the cases, more efficient than the generalized variance chart.

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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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Abid, Muhammad, Hafiz Zafar Nazir, Muhammad Riaz, and Zhengyan Lin. "In-control robustness comparison of different control charts." Transactions of the Institute of Measurement and Control 40, no. 13 (November 1, 2017): 3860–71. http://dx.doi.org/10.1177/0142331217734302.

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Control charts are widely used to monitor the process parameters. Proper design structure and implementation of a control chart requires its in-control robustness, otherwise, its performance cannot be fairly observed. It is important to know whether a chart is sensitive to disturbances to the model (e.g. normality under which it is developed) or not. This study, explores the robustness of Mixed EWMA-CUSUM (MEC) control chart for location parameter under different non-normal and contaminated environments and compares it with its counterparts. The robustness of the MEC scheme and counterparts is evaluated by using the run length distributions, and for better assessment not only is in-control average run length (ARL) used, but also standard deviation of run length (SDRL) and different percentiles – that is, 5th, 50th and 95th– are considered. A careful insight is necessary in selection and application of control charts in non-normal and contaminated environments. It is observed that the in-control robustness performance of the MEC scheme is quite good in the case of normal, non-normal and contaminated normal distributions as compared with its competitor’s schemes.

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Adeoti, Olatunde A. "On control chart for monitoring exponentially distributed quality characteristic." Transactions of the Institute of Measurement and Control 42, no. 2 (August 29, 2019): 295–305. http://dx.doi.org/10.1177/0142331219868595.

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The double exponentially weighted moving average (DEWMA) control chart has been observed to be more sensitive than the exponentially weighted moving average (EWMA) control chart for process monitoring assuming that the quality characteristic follows the normal distribution. In this paper, the DEWMA control chart is proposed for monitoring quality characteristics that follow the exponential distribution using variable transformation technique. The in-control and out-of-control average run lengths (ARLs) of the proposed control chart is obtained for equal and unequal smoothing constants. The performance of the proposed control chart with equal and unequal smoothing constants was investigated and compared with recent existing control charts in terms of the out-of-control average run lengths. Real life example is given to demonstrate the application of the proposed chart. The findings show that the performance of the proposed control chart outweighs existing control charts in the monitoring of process parameter when the quality variable follows exponential distribution for all shift sizes.

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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 existing charts. The proposed modified robust individuals control chart which incorporates the M-scale estimator in order to compute the process standard deviation offers substantial improvements over the existing median absolute deviation framework. With respect to the application in real data set, the proposed approach appears to perform better than the typical robust control chart, and outperforms other conventional charts particularly in the presence of contamination. Thus, it is for these reasons that the proposed modified robust individuals control chart is preferred especially when there is a possible existence of outliers in data collection process.

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Tiplica, Teodor, Abdessamad Kobi, and Alain Barreau. "Spectral Control Chart." Quality Engineering 17, no. 4 (October 2005): 695–702. http://dx.doi.org/10.1080/08982110500251287.

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Hanslik, T., PY Boelle, and A. Flahault. "The control chart." Public Health 115, no. 4 (July 2001): 277–81. http://dx.doi.org/10.1038/sj.ph.1900782.

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Dorais, Ann. "Fluency Control Chart." Word of Mouth 25, no. 4 (January 23, 2014): 13–15. http://dx.doi.org/10.1177/1048395013519576d.

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Alemi, Farrokh. "Tukeyʼs Control Chart." Quality Management in Health Care 13, no. 4 (October 2004): 216–21. http://dx.doi.org/10.1097/00019514-200410000-00004.

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Wu, Zhang, Ming Xie, Qingchuan Liu, and Yu Zhang. "SXC control chart." International Journal of Advanced Manufacturing Technology 30, no. 5-6 (January 3, 2006): 444–51. http://dx.doi.org/10.1007/s00170-005-0080-3.

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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 (November 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 cumulative sum control charts and the support vector data description algorithm, namely, high sensitivities to small shifts and learning abilities. The recommended values of some key parameters are also given for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the [Formula: see text] control chart. A real industrial case illustrates the application of the proposed control chart. The results also show that the [Formula: see text] control chart is more sensitive to small shifts than other traditional control charts (e.g. [Formula: see text] and multivariate cumulative sum) and a D control chart based on support vector data description.

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TAKIZAWA, Takayasu, Yoshio AGEMATSU, Kiyonori TAUCHI, Toshiji TOYOHARA, Isao IGARASHI, and Naomi MINOUCHI. "A Radar Control Chart—Handy Multivariate Control Chart—." JAPAN JOURNAL OF VETERINARY INFORMATICS 1989, no. 22 (1989): 17–24. http://dx.doi.org/10.2743/jve1986.1989.17.

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敬, 林. "Comparative Study of Shewhart Control Chart and CUSUM Control Chart." Open Journal of Nature Science 04, no. 03 (2016): 253–560. http://dx.doi.org/10.12677/ojns.2016.43030.

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Hong, Eui Pyo, Hae Woon Kang, Chang Wook Kang, and Jae Won Baik. "CV Control Chart Using GWMA Technique." Advanced Materials Research 337 (September 2011): 247–54. http://dx.doi.org/10.4028/www.scientific.net/amr.337.247.

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When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shifts in the magnitude of CV. This study suggest the CV-GWMA(generally weighted moving average) control chart, combining the GWMA technique, which shows better performance than the EWMA(exponentially weighted moving average) or DEWMA(double exponentially weighted moving average) technique in detecting small shifts of the process. Through a performance evaluation, the proposed control chart showed more excellent performance than the existing CV-EWMA control chart or the CV-DEWMA control chart in detecting small shifts in CV.

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Xin, Hua, Wan-Ju Hsieh, Yuhlong Lio, and Tzong-Ru Tsai. "Nonlinear Profile Monitoring Using Spline Functions." Mathematics 8, no. 9 (September 15, 2020): 1588. http://dx.doi.org/10.3390/math8091588.

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In this study, two new integrated control charts, named T2-MAE chart and MS-MAE chart, are introduced for monitoring the quality of a process when the mathematical form of nonlinear profile model for quality measure is complicated and unable to be specified. The T2-MAE chart is composed of two memoryless-type control charts and the MS-MAE chart is composed of one memory-type and one memoryless-type control charts. The normality assumption of error terms in the nonlinear profile model for both proposed control charts are extended to a generalized model. An intensive simulation study is conducted to evaluate the performance of the T2-MAE and MS-MAE charts. Simulation results show that the MS-MAE chart outperforms the T2-MAE chart with less false alarms during the Phase I monitoring. Moreover, the MS-MAE chart is sensitive to different shifts on the model parameters and profile shape during the Phase II monitoring. An example about the vertical density profile is used for illustration.

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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 outperforms the original 2-EWMA chart, as well as the original EWMA chart. In general, this article will help to enhance the detection effectiveness of the 2-EWMA chart, and facilitate its applications in SPC.

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Neubauer, Aljoscha Steffen. "The EWMA control chart: properties and comparison with other quality-control procedures by computer simulation." Clinical Chemistry 43, no. 4 (April 1, 1997): 594–601. http://dx.doi.org/10.1093/clinchem/43.4.594.

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Abstract A quality-control chart based on exponentially weighted moving averages (EWMA) has, in the past few years, become a popular tool for controlling inaccuracy in industrial quality control. In this paper, I explain the principles of this technique, present some numerical examples, and by computer simulation compare EWMA with other control charts currently used in clinical chemistry. The EWMA chart offers a flexible instrument for visualizing imprecision and inaccuracy and is a good alternative to other charts for detecting inaccuracy, especially where small shifts are of interest. Detection of imprecision with EWMA charts, however, requires special modification.

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Park, Young Soo, Eui Pyo Hong, Kyoung Yong Park, and Woong Hee Shon. "Performance Improvement Study of CV-EWMA Control Chart to Detect Small Shifts of CV." Advanced Materials Research 1051 (October 2014): 1016–22. http://dx.doi.org/10.4028/www.scientific.net/amr.1051.1016.

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In order to control a process that has short production cycle and where the product type and specifications change often with conventional shewhart control charts such as and control charts, a new control chart must be applied every time the parameters change . As this is a very inefficient method in terms of the cost and time, CV control chart using coefficient of variation statistics was developed. As CV control chart reflects only the current sample data on control chart, it can be useful when there is a significant change in process. However, it does not respond sensitively to a process that has subtle change or requires a high control level. CV-EWMA control chart was researched to monitor small shifts in CV. This study proposes a way to improve accuracy and precision of population parameter estimation of conventional CV-EWMA control chart and applied it to a control chart before analyzing its performance. As a result, the accuracy and precision of conventional CV-EWMA control chart has been improved and it was verified that the proposed control chart is a proper control chart to control small shifts of CV.

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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 (July 30, 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 of the proposed control chart.

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Resti, Aulia, Tatik Widiharih, and Rukun Santoso. "GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)." Jurnal Gaussian 10, no. 1 (February 28, 2021): 114–24. http://dx.doi.org/10.14710/j.gauss.v10i1.30938.

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Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL

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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 analysis of KM-chart was evaluated through a real industrial application. In a comparative study based on the average run length (ARL) criterion, KM-chart was compared with the kernel-distance based control chart, referred to as K-chart, and the k-nearest neighbor data description-based control chart, referred to as KNN-chart. Results revealed that, in terms of ARL, KM-chart performed better than KNN-chart in detecting small shifts in mean vector. Furthermore, the paper provides the MATLAB code for KM-chart, developed by the authors.

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Pekin Alakoc, N., and A. Apaydin. "A Fuzzy Control Chart Approach for Attributes and Variables." Engineering, Technology & Applied Science Research 8, no. 5 (October 13, 2018): 3360–65. http://dx.doi.org/10.48084/etasr.2192.

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The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

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Claro, Fernando A. E., Antonio F. B. Costa, and Marcela A. G. Machado. "Double sampling control chart for a first order autoregressive process." Pesquisa Operacional 28, no. 3 (December 2008): 545–62. http://dx.doi.org/10.1590/s0101-74382008000300008.

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In this paper we propose the Double Sampling control chart for monitoring processes in which the observations follow a first order autoregressive model. We consider sampling intervals that are sufficiently long to meet the rational subgroup concept. The Double Sampling chart is substantially more efficient than the Shewhart chart and the Variable Sample Size chart. To study the properties of these charts we derived closed-form expressions for the average run length (ARL) taking into account the within-subgroup correlation. Numerical results show that this correlation has a significant impact on the chart properties.

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Fisher, Paul R., and Royal D. Heins. "A Process-control Approach to Poinsettia Height Control." HortTechnology 5, no. 1 (January 1995): 57–63. http://dx.doi.org/10.21273/horttech.5.1.57.

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A methodology based on process-control approaches used in industrial production is introduced to control the height of poinsettia (Euphorbia pulcherrima L.). Graphical control charts of actual vs. target process data are intuitive and easy to use, rapidly identify trends, and provide a guideline to growers. Target reference values in the poinsettia height control chart accommodate the biological and industrial constraints of a stemelongation model and market specifications, respectively. A control algorithm (proportional-derivative control) provides a link between the control chart and a knowledge-based or expert computer system. A knowledge-based system can be used to encapsulate research information and production expertise and provide management recommendations to growers.

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BILEN, CANAN. "MONITORING AUTOCORRELATED PROCESSES WITH WAVELETS." International Journal of Reliability, Quality and Safety Engineering 17, no. 02 (April 2010): 133–56. http://dx.doi.org/10.1142/s021853931000372x.

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This paper develops a wavelet control chart for monitoring autocorrelated processes. The procedure uses the discrete wavelet transform of the original series, and traditional control charts are applied to the stream of wavelet coefficients. Unlike other control charts for monitoring autocorrelated processes found in the literature, the wavelet control chart does not require that a model be specified for the process data. The wavelet-based control chart is simple enough that it can be easily automated. Real and simulated data are used to illustrate the effectiveness of the proposed wavelet control chart.

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Noskievičová, Darja, and Tereza Smajdorová. "Proposal of Methodology for Practical Application of Nonparametric Control Charts." Quality Production Improvement - QPI 1, no. 1 (July 1, 2019): 464–71. http://dx.doi.org/10.2478/cqpi-2019-0063.

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Abstract This paper deals with the methodology for practical application of nonparametric control charts. This topic is very important for two reasons: firstly nonparametric control charts are very effective instruments for the realization of the statistical process monitoring phase I due to their robustness against various deviations from the data assumptions that must be met when applying model-based control charts. Secondly nonparametric control charts have very weak SW support and also they are not taught in the frame of training courses not even of the university study programmes. For that reason the practitioners do not know them and do not use them. The paper offers the proposal how to practically apply these control charts which is based on the complex simulation study of various nonparametric control charts performance when various data assumptions have not been met. The study has covered these nonparametric control charts: Shewhart sign control chart, nonparametric EWMA and nonparametric CUSUM control charts, nonparametric progressive mean control chart, control chart based on Mood statistics and robust median absolute deviation control chart. All charts have been studied in condition of not normally distributed data, autocorrelated data and data with nonconstant distribution parameters. The simulations were realized for statistically stable (IC – in control) and also statistically unstable (OC – out of control) processes. For the evaluation of the control charts performance median run length, 0.05-quantile, and 0.95-quantile were used.

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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 (April 23, 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 individual observations. In this work, we propose a new nonparametric Phase I control chart for monitoring the scale parameter based on the empirical likelihood ratio test. The simulation results show that the proposed chart is more effective than the existing charts in terms of signal probability. A real example is used to demonstrate how the proposed chart can be applied in practice.

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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 (March 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 traditional T2- and |S|-charts.

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Engler, J., K. H. Tölle, H. H. Timm, E. Hohls, and J. Krieter. "Control charts applied to pig farming data." Archives Animal Breeding 52, no. 3 (October 10, 2009): 272–83. http://dx.doi.org/10.5194/aab-52-272-2009.

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Abstract. Statistical control charts are effective tools to reveal changes in a production process. The CUSUM (cumulative sum) and the EWMA (exponentially weighted moving average) control chart are used to detect small deviations in a process. Data from two sow herds, herd A and herd B, were collected from 1999 to 2004. Farm A had an average number of 530 breeding sows, Farm B had an average of 370 breeding sows. Both herds were diagnosed with Porcine Reproductive and Respiratory Syndrome (PRRS). The weekly means of the number of piglets weaned (NPW), the pre-weaning mortality (PWM) and return to service rate (RSR) were analysed with different settings of the CUSUM as well as the EWMA control chart to reveal a shift in the production process. For the pre-weaning mortality and the number of piglets weaned, the two charts detected a change in the process 4 weeks (Farm A) and 2 weeks before (Farm B) PRRS was diagnosed. The CUSUM and the EWMA chart revealed a shift in the return to service rate on Farm A 3.5 months before PRRS was detected. On Farm B, the signal occurred 6 weeks before the infection was detected. The CUSUM and the EWMA control charts were effective tools for detecting small deviations in sow herd data. Compared with EWMA, the use of the CUSUM chart is more straightforward and the settings are more easily handled. The CUSUM chart is therefore the preferred option for use in practice.

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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 (May 2, 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. The control charts are usually implemented in two phases: Phase I (retrospective) and Phase II (prospective/monitoring). The efficiency of any control chart in Phase II depends on the accuracy of control limits obtained from Phase I. The current study focuses on the Phase I analysis of location parameters using median control charts. We examined the performance of different auxiliary information-based median control charts and compared the results with the usual median chart. Standardized variance and relative efficacy are used as performance measures to evaluate the efficiency of median estimators. Moreover, the probability to signal measure is used to evaluate the performance of proposed control charts to detect any potential changes in the process. The results revealed that the proposed auxiliary information based median control charts perform better in Phase I analysis. In addition, a practical illustration of an industrial scenario demonstrated the significance of the proposed control charts, in which the monitoring of concrete compressive strength is emphasized.

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Zhou, Maoyuan, and Wei Geng. "A Robust Control Chart for Monitoring Dispersion." Journal of Applied Mathematics 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/279203.

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Most robust control charts in the literature are for monitoring process location parameters, such as mean or median, rather than process dispersion parameters. This paper develops a new robust control chart by integrating a two-sample nonparametric test into the effective change-point model. Our proposed chart is easy in computation, convenient to use, and very powerful in detecting process dispersion shifts.

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Aslam, Muhammad, Nasrullah Khan, and Chi-Hyuck Jun. "A hybrid exponentially weighted moving average chart for COM-Poisson distribution." Transactions of the Institute of Measurement and Control 40, no. 2 (August 18, 2016): 456–61. http://dx.doi.org/10.1177/0142331216659920.

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We provide the complete design of a hybrid exponentially weighted moving average (HEWMA) control chart for COM-Poisson distribution. The necessary measures of the proposed control chart are given in this manuscript, and the average run lengths (ARLs) are determined through Monte Carlo simulation for various values of specified parameters. The performance of the proposed chart is compared with two existing control charts. The proposed chart is more efficient than these two existing charts in terms of ARLs; application of the proposed chart is described with the help of Montgomery’s data ( Introduction to Statistical Quality Control, John Wiley & Sons, New York, 2007).

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Hong, Eui Pyo, Hae Woon Kang, and Chang Wook Kang. "DEWMA Control Chart for the Coefficient of Variation." Advanced Materials Research 201-203 (February 2011): 1682–88. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.1682.

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When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. The CV-EWMA (exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts of CV. In this paper, we propose the CV-DEWMA control chart, combining the DEWMA (double exponentially weighted moving average) technique. We show that CV-DEWMA control chart perform better than CV-EWMA control chart in detecting small shifts when sample size n is larger than 5.

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Deng, Yu Hao, Hai Ping Zhu, Guo Jun Zhang, Hui Yin, and Fan Mao Liu. "Nonparametric Control Charts Design and Analysis for Small Lot Production Based on the Moving Average." Advanced Materials Research 988 (July 2014): 461–66. http://dx.doi.org/10.4028/www.scientific.net/amr.988.461.

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This paper designed a moving average sampling method for small samples, further designed moving average (MA) control chart and moving average cumulative sum (MACS) control chart respectively, and calculated the in-control and out-of-control average run length for both charts. The charts are robust, which can monitor the process state effectively without knowing the distribution. Through analyzing the control chart costs and quality loss that is related to the production lot size, the control chart parameters are reasonably optimized. By comparing the average run lengths and some numerical examples, the paper finds that MACS chart has a good performance on detecting small shift within the small samples under the nonparametric condition.

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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 (May 27, 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 of the Arcsine EWMA Chart are more reasonable than those of the new EWMA Chart. The Arcsine EWMA Chart is recommended if we are concerned with the proper values of the average run length.

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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 (November 16, 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 chart. Numerical simulation studies indicate that the AIB-SSEWMA chart has better detection ability than the existing SSEWMA and its competitive maximum EWMA based on auxiliary information (AIB-MaxEWMA) charts in view of average run lengths (ARLs). An illustrated example is used to demonstrate the efficiency of the proposed AIB-SSEWMA chart in detecting small process shifts.

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Wang, Hai Yu. "Statistical Process Control on Time Delay Feedback Adjustment Process." Advanced Materials Research 211-212 (February 2011): 305–9. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.305.

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Control chart can be designed to quickly detect small shifts in the mean of a sequence of independent normal observations. But this chart cannot perform well for autocorrelated process. The main goal of this article is to suggest a control chart method using to monitoring process with different time delay feedback controlled processes. A quality control model based on delay feedback controlled processes is set up. And the calculating method of average run length of control charts based on process output and control action of multiple steps delay MMSE feedback controlled processes is provided to evaluate control charts performance. A simple example is used to illustrate the procedure of this approach.

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Shahzad, Faisal, Zhensheng Huang, and Ambreen Shafqat. "The Design of GLR Control Chart for Monitoring the Geometric Observations Using Sequential Sampling Scheme." Symmetry 12, no. 12 (November 27, 2020): 1964. http://dx.doi.org/10.3390/sym12121964.

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The control charts’ design is focused on system forecasting which is important in mathematics and statistics; these techniques are commonly employed in manufacturing industries. The need for a control chart that can conceptualize and identify the symmetric or asymmetric structure of the monitoring phase with more than one aspect of the standard attribute is a necessity of industries. The generalized likelihood ratio (GLR) chart is a well-known method to track both the decrease and increase in the mechanism effectively. A control chart, termed as a GLR control chart, is established in this article, focusing on a sequential sampling scheme (the SS GLR chart) to evaluate the geometrically distributed process parameter. The SS GLR chart statistic is examined on a window of past samples. In contexts of the steady-state average time to signal, the output of the SS GLR control chart is analyzed and compared with the non-sequential geometric GLR chart and the cumulative sum (CUSUM) charts. In this article, the optimum parameter options are presented, and regression equations are established to calculate the SS GLR chart limits.

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DOKOUHAKI, PERSHANG, and RASSOUL NOOROSSANA. "SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1250083. http://dx.doi.org/10.1142/s0219519412500832.

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In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

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Arif, Osama H., and Muhammad Aslam. "A New Generalized Range Control Chart for the Weibull Distribution." Complexity 2018 (November 5, 2018): 1–8. http://dx.doi.org/10.1155/2018/9453589.

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In this study, a generalized range control chart is designed for the Weibull distribution using generally weighted moving average statistics. The proposed chart is based on minimum generally weighted moving average statistic and maximum generally weighted moving average statistics. We utilize the inverse erf function to transform the Weibull data to normal data. The necessary measures are given to assess the performance of the proposed control chart. The comparison study shows that the proposed control chart outperforms the existing control charts based on exponentially weighted moving average statistic in terms of the average run length. A real example is given for applying the proposed chart in the industry.

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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 (November 18, 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 VSIDS-AI chart are discussed. The steady-state average time to signal (ssATS) and steady-state expected average time to signal (ssEATS) are considered as the performance measures. The ssATS and ssEATS results of the VSIDS-AI chart are compared with those of the DS AI, variable sample size and sampling interval AI, exponentially weighted moving average AI (EWMA-AI) and run sum AI (RS-AI) charts. The results of comparison show that the VSIDS-AI chart outperforms the charts under comparison for all shift sizes, except the EWMA-AI and RS-AI charts for small shift sizes. An illustrative example is provided to demonstrate the implementation of the VSIDS-AI chart.

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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 (November 16, 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 the EEWMA control chart with the traditional EWMA and Shewhart control charts illustrates that the proposed control chart performs better in terms of quick detection of the shift. The working procedure of the proposed control chart was also illustrated by simulated and application data.

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Aslam, Muhammad, Ambreen Shafqat, G. Srinivasa Rao, Jean-Claude Malela-Majika, and Sandile C. Shongwe. "Multiple Dependent State Repetitive Sampling-Based Control Chart for Birnbaum–Saunders Distribution." Journal of Mathematics 2020 (October 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/8539361.

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This paper proposes a new control chart for the Birnbaum–Saunders distribution based on multiple dependent state repetitive sampling (MDSRS). The proposed control chart is a generalization of the control charts based on single sampling, repetitive sampling, and multiple dependent state sampling. Its sensitivity is evaluated in terms of the average run length (ARL) using both exact formulae and simulations. A comprehensive comparison between the Birnbaum–Saunders distribution control chart based on the MDSRS method and other existing competing methods is provided using a simulation study as well as a real-life illustration. The results reveal that the proposed chart outperforms the existing charts considered in this study by having better shift detection ability.

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Journal articles on the topic 'Chart of control'

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