Relevant bibliographies by topics / X-bar control chart

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'X-bar control chart'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "X-bar control chart"

1

Er Chiu, Jing. "A Fuzzy System for VSI X-Bar Control Chart." International Journal of Engineering and Technology 4, no. 4 (2012): 427–29. http://dx.doi.org/10.7763/ijet.2012.v4.402.

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Aslam, Muhammad, Ali Hussein AL-Marshadi, and Nasrullah Khan. "A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average." Mathematics 7, no. 10 (October 12, 2019): 957. http://dx.doi.org/10.3390/math7100957.

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The existing Shewhart X-bar control charts using the exponentially weighted moving average statistic are designed under the assumption that all observations are precise, determined, and known. In practice, it may be possible that the sample or the population observations are imprecise or fuzzy. In this paper, we present the designing of the X-bar control chart under the symmetry property of normal distribution using the neutrosophic exponentially weighted moving average statistics. We will first introduce the neutrosophic exponentially weighted moving average statistic, and then use it to design the X-bar control chart for monitoring the data under an uncertainty environment. We will determine the neutrosophic average run length using the neutrosophic Monte Carlo simulation. The efficiency of the proposed plan will be compared with existing control charts.
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Chan, Chan‐Ieong, Alan Ching Biu Tse, and Frederick H. K. Yim. "Comparing and combining individual x‐charts and x‐bar charts." International Journal of Quality & Reliability Management 20, no. 7 (October 1, 2003): 827–35. http://dx.doi.org/10.1108/02656710310491230.

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Control charts have played an important role in monitoring the performance of operation processes, ever since their invention. Traditionally, according to Juran's idea and others, x‐bar charts are more sensitive than individual x‐charts. However, such a conclusion is valid only under certain conditions. Individual x‐charts can outperform x‐bar charts in some situations, especially in cases of minor and extreme changes of the center value. Since each chart has its own advantages and disadvantages, the idea of combining the results of these two charts is studied. The finding seems to be useful for practitioners in quality control.
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Rahn, G. E., S. G. Kapoor, and R. E. DeVor. "Single-Subgroup Performance Measures and Diagnostic Procedures for X-Bar Control Charts." Journal of Engineering for Industry 116, no. 2 (May 1, 1994): 216–24. http://dx.doi.org/10.1115/1.2901933.

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Although Shewhart control charts have had a tremendous impact on quality improvement, the inability to precisely measure chart performance has limited their role, and subsequently overall effectiveness in the control of manufacturing processes. Measures of performance in terms of operational characteristics (OC) are defined on two distinct levels: (a) single-subgroup level, which examines the probability of a rule violation at any given subgroup (b) multiple-subgroup level, which considers the probability of one or more rule violations throughout process monitoring. Single-subgroup performance measures for X-bar charts that employ four rules are formulated. These measures are exact expressions of operational characteristics, except for the numerical approximation to the integral of the normal distribution. Applications of these models to simulated data demonstrate their accuracy in predicting chart performance. In addition, a diagnostic methodology is described which utilizes the derived performance measures to predict the mean of a shifted distribution. The proposed diagnostic procedure is illustrated in validation and application examples.
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Safaei, Abdul Sattar, Reza Baradaran Kazemzadeh, and Heng-Soon Gan. "Robust economic-statistical design of X-bar control chart." International Journal of Production Research 53, no. 14 (March 2, 2015): 4446–58. http://dx.doi.org/10.1080/00207543.2015.1018449.

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Prajapati, D. R., and Sukhraj Singh. "Determination of level of correlation for products of pharmaceutical industry by using modified X-bar chart." International Journal of Quality & Reliability Management 33, no. 6 (June 6, 2016): 724–46. http://dx.doi.org/10.1108/ijqrm-05-2014-0053.

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Purpose – It is found that the process outputs from most of the industries are correlated and the performance of X-bar chart deteriorates when the level of correlation increases. The purpose of this paper is to compute the level of correlation among the observations of the weights of tablets of a pharmaceutical industry by using modified X-bar chart. Design/methodology/approach – The design of the modified X-bar chart is based upon the sum of χ2s, using warning limits and the performance of the chart is measured in terms of average run lengths (ARLs). The ARLs at various sets of parameters of the modified X-bar chart are computed; using MATLAB software at the given mean and standard deviation. Findings – The performance of the modified X-bar chart is computed for sample sizes of four. ARLs of optimal schemes of X-bar chart for sample size of four are computed. Various optimal schemes of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.00, 0.25, 0.50, 0.75 and 1.00 are presented in this paper. Samples of weights of the tablets are taken from a pharmaceutical industry and computed the level of correlation among the observations of the weights of the tablets. It is found that the observations are closely resembled with the simulated observations for the level of correlation of 0.75 in this case study. The performance of modified X-bar chart for sample size (n) of four at the levels of correlation (Φ) of 0.50 and 0.75 is also compared with the conventional (Shewhart) X-bar chart and it is concluded that the modified X-bar chart performs better than Shewhart X-bar chart. Research limitations/implications – All the schemes are optimized by assuming the normal distribution. But this assumption may also be relaxed to design theses schemes for autocorrelated data. The optimal schemes for modified X-bar chart can also be used for other industries; where the manufacturing time of products is small. This scheme may also be used for any sample sizes suitable for the industries Practical implications – The optimal scheme of modified X-bar chart for sample size (n) of four is used according to the computed level of correlation in the observations. The simple design of modified X-bar chart makes it more useful at the shop floor level for many industries where correlation exists. The correlation among the process outputs of any industry can be find out and corresponding to that level of correlation, the suggested control chart parameters can be used. Social implications – The design of modified X-bar chart uses very less numbers of parameters so it can be used at the shop floor level with ease. The rejection level of products in the industries can be reduced by designing the better control chart schemes which will also reduce the loss to the society as suggested by Taguchi (1985). Originality/value – Although; it is the extension of previous work but it can be applied to various manufacturing and service industries; where the data are correlated and normally distributed.
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Bakir, Saad T. "A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution." International Journal of Quality, Statistics, and Reliability 2012 (September 11, 2012): 1–10. http://dx.doi.org/10.1155/2012/147520.

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This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.
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Lee, Sang-Ho, and Chi-Hyuck Jun. "A New Control Scheme Always Better Than X-Bar Chart." Communications in Statistics - Theory and Methods 39, no. 19 (September 24, 2010): 3492–503. http://dx.doi.org/10.1080/03610920903243744.

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CASTAGLIOLA, PHILIPPE. "$\bar{X}$ CONTROL CHART FOR SKEWED POPULATIONS USING A SCALED WEIGHTED VARIANCE METHOD." International Journal of Reliability, Quality and Safety Engineering 07, no. 03 (September 2000): 237–52. http://dx.doi.org/10.1142/s0218539300000201.

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This paper proposes a new [Formula: see text] control chart devoted to the monitoring of skewed populations. This control chart, called the Scaled Weighted Variance[Formula: see text] control chart (SWV [Formula: see text] for short), is an improvement of the Weighted Variance [Formula: see text] control chart proposed by Choobineh and Ballard in 1987. In this paper we derive the control limits of the SWV [Formula: see text] control chart and give an illustrative example. Comparisons in terms of type I error and ARL are performed in the case of a lognormal, gamma and Weibull population.
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Oprime, Pedro Carlos, Naijela Janaina da Costa, Carlos Ivan Mozambani, and Celso Luiz Gonçalves. "X-bar control chart design with asymmetric control limits and triple sampling." International Journal of Advanced Manufacturing Technology 104, no. 9-12 (September 13, 2018): 3313–26. http://dx.doi.org/10.1007/s00170-018-2640-3.

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Dissertations / Theses on the topic "X-bar control chart"

1

Nam, Kyungdoo T. "A Heuristic Procedure for Specifying Parameters in Neural Network Models for Shewhart X-bar Control Chart Applications." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278815/.

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This study develops a heuristic procedure for specifying parameters for a neural network configuration (learning rate, momentum, and the number of neurons in a single hidden layer) in Shewhart X-bar control chart applications. Also, this study examines the replicability of the neural network solution when the neural network is retrained several times with different initial weights.
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Harvey, Martha M. (Martha Mattern). "The Fixed v. Variable Sampling Interval Shewhart X-Bar Control Chart in the Presence of Positively Autocorrelated Data." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278763/.

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This study uses simulation to examine differences between fixed sampling interval (FSI) and variable sampling interval (VSI) Shewhart X-bar control charts for processes that produce positively autocorrelated data. The influence of sample size (1 and 5), autocorrelation parameter, shift in process mean, and length of time between samples is investigated by comparing average time (ATS) and average number of samples (ANSS) to produce an out of control signal for FSI and VSI Shewhart X-bar charts. These comparisons are conducted in two ways: control chart limits pre-set at ±3σ_x / √n and limits computed from the sampling process. Proper interpretation of the Shewhart X-bar chart requires the assumption that observations are statistically independent; however, process data are often autocorrelated over time. Results of this study indicate that increasing the time between samples decreases the effect of positive autocorrelation between samples. Thus, with sufficient time between samples the assumption of independence is essentially not violated. Samples of size 5 produce a faster signal than samples of size 1 with both the FSI and VSI Shewhart X-bar chart when positive autocorrelation is present. However, samples of size 5 require the same time when the data are independent, indicating that this effect is a result of autocorrelation. This research determined that the VSI Shewhart X-bar chart signals increasingly faster than the corresponding FSI chart as the shift in the process mean increases. If the process is likely to exhibit a large shift in the mean, then the VSI technique is recommended. But the faster signaling time of the VSI chart is undesirable when the process is operating on target. However, if the control limits are estimated from process samples, results show that when the process is in control the ARL for the FSI and the ANSS for the VSI are approximately the same, and exceed the expected value when the limits are fixed.
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Kimura, Erin A. "RELIABILITY ANALYSIS OF LOW-SILVER BGA SOLDER JOINTS USING FOUR FAILURE CRITERIA." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/867.

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The appropriate selection of failure criterion for solder joint studies is necessary to correctly estimate reliability life. The objective of this study is to compare the effect of different failure criteria on the reliability life estimation. The four failure criteria in this study are a 20% resistance increase defined in the IPC-9701A standard, a resistance beyond 500 Ω, an infinite resistance (hard open), and a failure criterion based on X-bar and R control charts. Accelerated thermal cycling conditions of a low-silver BGA study included 0°C to 100 °C with ten minute dwell times and -40°C to 125°C with ten minute dwell times. The results show that the life estimation based on X-bar and R failure criterion is very similar to the life estimation when a 20% resistance increase defined in the IPC-9701A failure criterion is used. The results also show that the reliability life would be overestimated if the failure criterion of a resistance threshold of 500 Ω or an infinite resistance (hard open) is used.
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Lin, Hung —. Chia, and 林宏嘉. "Revised X-bar Control Chart." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/p6dv4s.

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碩士
淡江大學
統計學系
91
This paper presents two approaches for constructing control limits of X-bar control chart that can enable the user to begin monitoring the process mean at an earlier stage than the standard approaches. The proposed control limits can be constructed easily and are closed to any specific percentile of run length distribution of the true limits, even when only a few initial subgroups are available. Performances of the proposed approaches are studied by Monte Carlo simulation. The simulation results show that the proposed control limits perform similarly to the true limits even when the limits are estimated using data from only a few initial subgroups.
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Hsu, Shih-Hsueh, and 徐仕學. "Moving weight average X-bar control chart with variable sampling intervals." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/10866655187044317525.

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碩士
國立雲林科技大學
工業工程與管理研究所碩士班
94
Reynolds[1998] proposed the standard X-bar control chart with variable sampling intervals (STD VSI X-bar) which is an effective monitoring method. If the newest sample mean falls in the warning region, a short sampling interval is used in the next sampling, whereas a long sampling interval is used. However, compared to other adaptive X-bar control charts, STD VSI X-bar is insensitive to the moderate and small process shift. The reason is that the switching rule of STD VSI X-bar only refers to the newest sample mean to choose the sampling interval. In order to overcome the drawback of the switching rule of STD VSI X-bar, the moving weight average method is applied to give the equal weight to the samples of the recent periods. The moving weight average value is treated as the criterion of choosing the sampling intervals. It is called ‘Moving weight average X-bar control chart with variable sampling intervals, MWA VSI X-bar. The results of the paper show that MWA VSI X-bar not only increases the ability of monitoring the moderate and small process shift but also reduces the average number of switches.
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Hung, Pei-Yi, and 洪蓓怡. "Economic Design of Variable Sampling Intervals X-bar and R Control Chart." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/52198354504910617532.

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碩士
國立雲林科技大學
工業工程與管理研究所碩士班
92
The control chart makes the monitoring in process stability to reduce defective production and it can use to estimate process parameters. Then through these messages to determine process production or provide effective information in process improvement, so the control chart is a good tool to solve question and improve quality. The process control must simultaneously maintain the process mean and the process variation, so can help the performers to understand the actual condition about entire process, therefore, in this paper, we uses X-bar and R control chart to monitor process. In this paper, we develop the economic design of the variable sampling intervals（VSI）X-bar and R control chart to determine the values of seven test parameters of the chart, i.e. the sampling size（n）, the sampling interval（h1、h2）, the control limits coefficients（L1、L2）, and the warning limit coefficients（L3、L4）. The purpose is let the expected total cost minimum associated with the test procedure. The genetic algorithm（GA）is used to search for the optimal values of the seven test parameters of VSI X-bar and R control chart, and an example is provided to interpret the solution procedure. And then carried out sensitivity analysis to investigate the effects of model parameters on the solution of the economic design as the basis for making decision.
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Yi-RuJhuo and 卓怡如. "Setting Control Limits of X-bar Control Chart Subjected to Short-term Human Resources." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2jh56g.

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Liou, Jia-Hueng, and 劉家宏. "Non-Normality of the Joint Economic Design of X-bar and R Control Chart." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/24656584001280333879.

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碩士
國立雲林科技大學
工業工程與管理研究所
87
Since Duncan’s pioneering work in economically design of X-bar control chart, there were a lot of works toward economically design of different control charts. Saniga who is the first person proposed joint economically optimal design of X-bar and R control chart in 1977. In his research, the quality characteristic is assumed to be normally distributed. But there are cases to have quality characteristic that is not normally distributed in practice. In this research, the Burr distribution is used to represent the distribution of the quality characteristic which is nonnormally distributed, and Saniga’s joint economic design model is used as the basis for developing the joint economic design of X-bar and R control chart. The Genetic Algorithms procedure is employed for searching the optimal solution of those economic design parameters of X-bar and R control chart. A computer program will be developed also to help the practitioner for searching the optimal design parameters. There are two points must be considered before making use of this study, which are described in the following list. 1. The distribution of the quality characteristic of this study that must can be approximated by Burr distribution. 2. To understand the condition of the non-normal distribution of the quality characteristic in advance, and to obtain the skewness coefficient and the kurtosis coefficient of the non-normal distribution before making use of this study. 12 categories of non-normal distribution, and each category includes 81examples are presented for optimal solution in this research. This research found that if the normal model is performed but the distribution of the quality characteristic is nonnormally distributed in practice, the false alarm and the expected cost per unit of output of normal model are more then this research.
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Hung, Shih-Han, and 洪士涵. "A study of Detecting the Autocorrelated Process by Variable Parameters x-bar Control Chart." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/59392960850452646675.

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碩士
國立雲林科技大學
工業工程與管理研究所碩士班
95
The traditional statistic process control uses the independent and normal data to determine whether the process has any anomalism situation. It will cause the increase of the false rate of control chart if we use the traditional independent control chart to detect the process. So it is an important issue about how to use the control chart to detect the process effectively when the autocorrelated exists in the process. So this study discusses all the control parameters and investigates the performance of the variable control parameters be used for detecting the autocorrelated process. This study divides the degree of autocorrelated into low, medium and high. No matter how the process correlation is, the VP control chart has a faster speed than the other control chart when they detect the small and medium departure of the process. For getting better detecting speed and sample cost, you should choose the bigger n1 and n2 when detecting the low autocorrelated process or small departure. The bigger control limit coefficient should be chosen when detecting the small departure. The smaller control limit coefficient should be chosen when detecting the big departure. The sample interval, samples, and the coefficient of control limit in high correlated process have no effect on the detecting speed.
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Lin, Kung-Hong, and 林昆宏. "Non-Normality of the Joint Economic Design of X-bar and S Control Chart." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/56180967728341392283.

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碩士
國立雲林科技大學
工業工程與管理研究所碩士班
91
Traditionally, observations characteristic is assumed to be normally distributed when control chart is applied for statistic process control. If observations value is not normally distributed, the traditional methods of design about the control chart probably reduce the ability that control chart detects non-chance cause. In according to Burr distribution, Hooke and Jeeves optimal searching rule and the skill of computer simulation, this research develops the joint economic design model of X-bar and S control chart under non-normally distributed. The theme of the thesis discuss that X-bar and S control chart control average and variance about process quality in the same time with Knappenberger and Grandage’s(1969) cost model; besides, it also proposes the economic design to make the max profit on each unit. The purposes of this research are described in the following list: 1. Apply non-normal distributed to the joint of the economic design of X-bar and S control chart. 2. Develop non-normal distributed on control limit to the joint of the economic design of X-bar and S control chart. 3. Optimal solution in different (c,k) and make sensitive analysis.
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Books on the topic "X-bar control chart"

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Chokethaworn, Nantawong. An economic comparison of X[bar], cumulative sum and geometric moving average control charts for controlling process mean. 1986.

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Book chapters on the topic "X-bar control chart"

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Nieckula, Jacek. "Frequency Distribution Supporting Recognition of Unnatural Patterns on Shewhart X-bar Chart." In Frontiers in Statistical Quality Control 6, 102–17. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-642-57590-7_8.

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Saniga, Erwin, Darwin Davis, Alireza Faraz, Thomas McWilliams, and James Lucas. "Characteristics of Economically Designed CUSUM and $$\bar{X}$$ Control Charts." In Frontiers in Statistical Quality Control 11, 201–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12355-4_13.

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Rahim, M. A. "Economically Optimal Design of Inline Equation $$ \bar X$$ -Control Charts Assuming Gamma Distributed In-Control Times." In Optimization in Quality Control, 175–96. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6151-4_5.

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Lam, K. K., and M. A. Rahim. "Joint Determination of Economic Design of % MathType!MTEF!2!1!+- % feaagaart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn % hiov2DGi1BTfMBaeXatLxBI9gBamXvP5wqonvsaeHbd9wDYLwzYbqe % e0evGueE0jxyaibaieYlf9irVeeu0dXdh9vqqj-hEeeu0xXdbba9fr % Fj0-OqFfea0dXdd9vqaq-JfrVkFHe9pgea0dXdar-Jb9hs0dXdbPYx % e9vr0-vr0-vqpWqaaeaabiGaciaacaqabeaadaqaaqaaaOqaaeHbfv % 3ySLgzaGqbciqb-Hfayzaaraaaaa!3AD9! $$ \bar X $$ -Control Charts, Economic Production Quantity, and Production Run Length for a Deteriorating Production System." In Frontiers in Statistical Quality Control 7, 58–78. Heidelberg: Physica-Verlag HD, 2004. http://dx.doi.org/10.1007/978-3-7908-2674-6_5.

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Conference papers on the topic "X-bar control chart"

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Ching-Pou, Chang,. "Effect of Preventive Maintenance on the Economic Design of X-Bar Control Chart." In Information Control Problems in Manufacturing, edited by Bakhtadze, Natalia, chair Dolgui, Alexandre and Bakhtadze, Natalia. Elsevier, 2009. http://dx.doi.org/10.3182/20090603-3-ru-2001.00284.

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Teoh, Wei Lin, and Michael B. C. Khoo. "A study on the run sum X-bar control chart with unknown parameters." In INTERNATIONAL CONFERENCE ON FUNDAMENTAL AND APPLIED SCIENCES 2012: (ICFAS2012). AIP, 2012. http://dx.doi.org/10.1063/1.4757494.

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Hassan, Adnan. "Ensemble ANN-based recognizers to improve classification of X-bar control chart patterns." In 2008 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2008. http://dx.doi.org/10.1109/ieem.2008.4738221.

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Madzík, Peter. "The Effect of Non-Normal Distributions on the Control Limits of X-Bar Chart." In International Scientific Days 2018. Wolters Kluwer ČR, Prague, 2018. http://dx.doi.org/10.15414/isd2018.s3.11.

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Li, Yaping, Ershun Pan, and Zhen Chen. "Considering machine health condition in jointly optimizing predictive maintenance policy and X-bar control chart." In 2017 International Conference on Grey Systems and Intelligent Services (GSIS). IEEE, 2017. http://dx.doi.org/10.1109/gsis.2017.8077727.

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Li, F. C., C. H. Yeh, and P. K. Wang. "An x-bar control chart economic design sampling strategy for non-normally distributed data under gamma (λ, 2) failure models." In 2008 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2008. http://dx.doi.org/10.1109/ieem.2008.4737869.

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Li, Feng-Chia, Tzn-Chin Chao, Li-Lon Yeh, and Yen-Fu Chen. "The restrictions in economic design model for an x-bar control chart under non-normally distributed data with Weibull shock model." In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2009. http://dx.doi.org/10.1109/ieem.2009.5373240.

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Lee, H. J., T. J. Lim, and S. C. Jang. "VSSI $\bar X$ control charts for processes with multiple assignable causes." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419390.

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Fong-Jung, Yu,. "An Economic-Statistical Design of X-Bar Control Charts Using Taguchi Loss Functions." In Information Control Problems in Manufacturing, edited by Bakhtadze, Natalia, chair Dolgui, Alexandre and Bakhtadze, Natalia. Elsevier, 2009. http://dx.doi.org/10.3182/20090603-3-ru-2001.00287.

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Huifen Chen and Wei-Lun Kuo. "Comparisons of the symmetric and asymmetric control limits for $\bar X$ charts." In 2007 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2007. http://dx.doi.org/10.1109/ieem.2007.4419418.

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