共查询到19条相似文献，搜索用时 46 毫秒
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传统Shewhart-
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为了有效地判别GPS异常监测数据,建立了GPS监测序列异常检验的数学模型,提出利用统计过程控制中的控制图对监测序列进行异常检验和预警的新方法;针对GPS监测数据不服从正态分布的问题,提出利用累积分布函数的核密度估计将其转换为Q统计量,并以此为基础构建基于Q统计量的控制图用于GPS异常波动数据的检验;该文文末利用仿真数据对比分析了休哈特控制图与累积和控制图对不同异常偏移值的检验效果,结果表明两种控制图各有利弊、相互补充,休哈特控制图对于3倍以上标准差的异常偏移能够给出有效的预警,但缺乏小偏移检测的能力,累积和控制图能够精确检测出最小达0.5倍标准差的连续小偏移,但是随着偏移值的增大其误警率会有所增加。 相似文献
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采用休哈特控制图的方法可以对测量过程是否处于统计控制状态进行控制.但是,对于平均值控制界限的确定则认为不妥,根本原因是,平均值控制界限依据重复性条件下所得数据的标准偏差来设置,用它来控制复现性条件下子组的平均值的波动性,由于条件不同,因此,前者不能来控制后者.依据计量保证方案(MAP)提出的控制界限的确定,是用在复现性条件下各子组的平均值的组间标准差来设置控制界限,也就是用组内平均值的分散性来控制组内平均值.通过对二等电阻、直流电压和电能表标准装置同一组试验数据进行上述两种控制图的制作比较,以及对确定控制界限的理论分析,证明了休哈特控制图平均值控制界限的确定不妥. 相似文献
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以平均产品长度(APL)为评价控制图性能的标准,研究了偏态控制图的优化设计问题.针对一般控制图无法有效解决偏态总体的不对称性的情况,采用赋权方差法来构造非对称的偏态控制图,并获得其最优设计模型;最后给出了模型的灵敏度分析及算例. 相似文献
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为了提高用于高质量过程监控的累积计点值控制图的监控效率,提出了一类采用多点报警准则的高质量过程缺陷率控制图设计,并给出了计算这类改进型控制图的平均运行长度的马尔科夫链方法。为了验证该设计方法的改进效果,分析比较了采用多点报警规则的3种累积计点值控制图（报警规则为连续两点出界、连续三点中有两点出界、连续三点出界）与报警规则不改变的（报警规则为有一个点出界）累积计点值控制图的监控效率。比较结果表明：在受控状态下平均运行长度都为370的情况下,多点报警的累积计点值控制图的失控状态下的平均运行长度分别较报警规则不变的控制图的平均运行长度减小了约44%、40%、63%,控制图发现缺陷率增大的效率明显改善。得出结论：这种多点报警的累积计点值控制图发现缺陷率增大的速度提高40%~60%。 相似文献
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Murat C. Testik George C. Runger Connie M. Borror 《Quality and Reliability Engineering International》2003,19(1):31-38
In this paper, the robustness of the multivariate exponentially weighted moving average (MEWMA) control chart to non‐normal data is examined. Two non‐normal distributions of interest are the multivariate distribution and the multivariate gamma distribution. Recommendations for constructing MEWMA control charts when the normality assumption may be violated are provided. Copyright © 2002 John Wiley & Sons, Ltd. 相似文献
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Abdul Haq 《Quality and Reliability Engineering International》2017,33(7):1629-1631
A new hybrid exponentially weighted moving average (HEWMA) control chart has been proposed in the literature for efficiently monitoring the process mean. In that paper, the computed variance of the HEWMA statistic was, unfortunately, not correct! In this discussion, the correct variance of the HEWMA statistic is given, and the run length characteristics of the HEWMA control chart are studied and explored. It is noticed that not only the superiority of the HEWMA control chart remains over the existing (considered before) charts but also the new results based on the corrected control limits are more profound and reflective. Copyright © 2016 John Wiley & Sons, Ltd. 相似文献
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Burcu Aytaolu
zlem Türker Bayrak 《Quality and Reliability Engineering International》2019,35(7):2429-2441
The number of studies about control charts proposed to monitor profiles, where the quality of a process/product is expressed as function of response and explanatory variable(s), has been increasing in recent years. However, most authors assume that the in‐control parameter values are known in phase II analysis and the error terms are normally distributed. These assumptions are rarely satisfied in practice. In this study, the performance of EWMA‐R, EWMA‐3, and EWMA‐3(d2) methods for monitoring simple linear profiles is examined via simulation where the in‐control parameters are estimated and innovations have a Student's t distribution or gamma distribution. Instead of the average run length (ARL) and the standard deviation of run length, we used average and standard deviation of the ARL as performance measures in order to capture the sampling variation among different practitioners. It is seen that the estimation effect becomes more severe when the number of phase I profiles used in estimation decreases, as expected, and as the distribution deviates from normality to a greater extent. Besides, although the average ARL values get closer to the desired values as the amount of phase I data increases, their standard deviations remain far away from the acceptable level indicating a high practitioner‐to‐practitioner variability. 相似文献
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Yi Dai Yunzhao Luo Zhonghua Li Zhaojun Wang 《Quality and Reliability Engineering International》2011,27(7):877-884
We propose a new multivariate CUSUM control chart, which is based on self adaption of its reference value according to the information from current process readings, to quickly detect the multivariate process mean shifts. By specifying the minimum magnitude of the process mean shift in terms of its non‐centrality parameter, our proposed control chart can achieve an overall performance for detecting a particular range of shifts. This adaptive feature of our method is based on two EWMA operators to estimate the current process mean level and make the detection at each step be approximately optimal. Moreover, we compare our chart with the conventional multivariate CUSUM chart. The advantages of our control chart detection for range shifts over the existing charts are greatly improved. The Markovian chain method, through which the average run length can be computed, is also presented. Copyright © 2010 John Wiley & Sons, Ltd. 相似文献
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The performance of a control chart is completely characterized by its run length distribution. Quality practitioners usually do not have access to the run length distribution but rely on the average run length (ARL) to design and evaluate the performance of an exponentially weighted moving average (EWMA) control chart. This article presents a web-based tool that provides users easy access to the Phase 2 (online or monitoring phase) run length distribution for a two-sided EWMA control chart with known parameters. The web-based tool calculates the run length distribution, percentiles of the run length distribution, as well as the mean (ARL) and variance (VRL) of the run length distribution. Additional functionality of the web-based tool includes plotting the run length distribution functions, building tables of the quantiles of the run length distribution, finding the smoothing parameter (λ) for an EWMA control chart for fixed control limit that satisfies ARL, VRL or percentile performance, and finding the control chart limit (k) for an EWMA control chart that satisfies ARL, VRL, or percentile performance. This tool and these techniques enable quality practitioners to better design and evaluate EWMA control charts. 相似文献
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Vasileios Alevizakos Kashinath Chatterjee Christos Koukouvinos 《Quality and Reliability Engineering International》2021,37(6):2622-2645
Nonparametric (or distribution-free) control charts are used for monitoring processes where there is a lack of knowledge about the underlying distribution. In this article, a triple exponentially weighted moving average control chart based on the signed-rank statistic (referred as TEWMA-SR chart) is proposed for monitoring shifts in the location parameter of an unknown, but continuous and symmetric, distribution. The run-length characteristics of the proposed chart are evaluated performing Monte Carlo simulations. A comparison study with other existing nonparametric control charts based on the signed-rank statistic, the TEWMA sign chart, and the parametric TEWMA-
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Monitoring changes in the Weibull mean and variance simultaneously is of interest in quality control. The mean and variance of a Weibull process are determined by its shape and scale parameters. Most studies are focused on monitoring the Weibull scale parameter with fixed shape parameter or the Weibull shape parameter with fixed scale parameter. In this paper, we propose an exponentially weighted moving average chart based on the likelihood‐ratio test and an inverse error function called ELR chart to monitor changes in the Weibull mean and variance simultaneously. The simulation approach is used to derive the average run length. We compare our proposed chart with other existing control charts for 3 cases, including scale parameter changes with fixed shape parameter, shape parameter changes with fixed scale parameter, and both parameters changes. The results show that the ELR chart outperforms the other control charts in terms of average run length in most cases. Two numerical examples are used to illustrate the applications of the proposed control chart. 相似文献
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The cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts have been widely accepted because of their fantastic speed in identifying small‐to‐moderate unusual variations in the process parameter(s). Recently, a new CUSUM chart has been proposed that uses the EWMA statistic, called the CS‐EWMA chart, for monitoring the process variability. On similar lines, in order to further improve the detection ability of the CS‐EWMA chart, we propose a CUSUM chart using the generally weighted moving average (GWMA) statistic, named the GWMA‐CUSUM chart, for monitoring the process dispersion. Monte Carlo simulations are used to compute the run length profiles of the GWMA‐CUSUM chart. On the basis of the run length comparisons, it turns out that the GWMA‐CUSUM chart outperforms the CUSUM and CS‐EWMA charts when identifying small variations in the process variability. A simulated dataset is also used to explain the working and implementation of the CS‐EWMA and GWMA‐CUSUM charts. 相似文献