CUSUM控制图的一种优化设计方法研究

提出了一种以田口质量损失函数最小为目标的CUSUM控制图优化设计方法，建立了优化设计理论模型，提出了优化分析的流程，进行了实例分析和验证。     

经济生产批量与CUSUM控制图最优化联合设计

针对传统经济生产批量一般以完美生产过程为对象,质量成本通常也仅以符合规格为判断的标准等问题,提出了一种考虑田口损失函数的将经济生产批量与累积和控制图联合设计的模型。对于生产过程,提出了一种更符合实际情况的三阶段的制造过程,建立了考虑均值随机偏移的三阶段损失模型,并以此为基础,建立了综合考虑包括质量损失及与批量相关的储存成本、订货成本等在内的总期望成本,目的是获得最优化的经济生产批量和累积以及控制图参数设计联合模型,并使生产过程的总期望成本最小。采用行径搜索法对模型进行求解,并举出数值例子进行比较和参数敏感度分析加以说明模型的应用情况。     

基于CUSUM控制图和SVM的单病种成本差异分析

CUSUM控制图可用来监测均值的偏移.在单病种成本值发生重大差异前,短期内会发生一定失控趋势,CUSUM控制图却不能及时发出失控信号.针对成本差异分析中存在的问题,提出了基于支持向量机(SVM)的智能化单病种成本差异分析模型来替代CUSUM控制图.首先利用支持向量机(SVM)自身良好的泛化能力经过训练后获得复杂成本值之间的内在规律,然后对测试样本进行有效的预测分类,及时给出失控的样本点.将模型应用到单纯性阑尾炎病人样本中,与CUSUM控制图方法作比较,结果显示了该模型比CUSUM控制图更加及时发现有失控趋势或失控的样本点.     

控制图在检测实验室质量控制中的应用

0前言 在ISO/IEC 17025<检测和校准实验室能力的通用要求>中,技术要求的第9个要素"检测和校准结果质量的保证"(即5.9条)要求实验室在实际可行的情况下采用统计技术对检测结果数据进行技术核查,以监控检测的有效性,预防问题的发生.目前,各检测实验室正开始积极探索统计技术在检测质量控制方面的应用.本文通过运用 (-X)-R控制图分析钢勾心抗弯刚度的检测数据,说明控制图在检测实验室质量控制中的应用.     

控制图在工业生产测量过程中的应用

本文通过对控制图的简略介绍及其在工业生产测量过程控制中应用情况的分析探讨,以期推动计量检测工作质量控制水平的不断提高。     

控制图在测量过程中的应用

本文主要介绍了控制图的控制对象、建立方法、控制量和控制限的计算及控制图的应用,对如何应用日常检测数据建立控制图控制质量及测量过程具有指导意义。     

SPC控制图在保持架生产质量控制中的应用

为保障滚动轴承保持架产品质量,对保持架机械性能采取SPC控制图统计分析,制定生产过程预警线,实现了对注塑生产过程状态和质量的异常波动的报警,帮助公司降低因保持架机械性能不合格造成的经济损失,保证保持架产品质量品质稳定,增强了企业的市场竞争力.     

控制图在扭矩扳子比对过程中的应用

通过建立控制图分析了扭矩扳子在比对过程中的质量控制结果。主要介绍了平均值-标准偏差控制图（xˉ-s图）的应用,以监视比对过程中数据的质量;根据控制图提供异常值的有关信息,采取相应纠正措施,使比对的测量过程处于统计控制状态。文章还从选择合适的控制图、测量频次的确定、绘制控制图的顺序、失控后的处置等方面分析讨论了实际操作中需要注意的问题,为计量人员开展扭矩扳子的测量工作提供参考。     

控制图在实验室内部质量控制中的应用

阐述质控图的制作方法和技巧,采用实例分析方法对实验室分析质量控制图的绘制和应用进行了较详细的研究和分析。     

Beyond the Nomogram: Rapid Selection of Parameters for a CUSUM Chart

The cumulative sum chart is widely employed in quality control. These charts are designed to exhibit acceptable average run lengths both when the process is in and out of control. This paper introduces a technique for parameter selection for such a chart for specified average run lengths. It is essentially an updating of the well-known nomogram technique.     

与控制点方法论相结合的V-mask累积和控制图

V-mask累积和控制图虽然能够有效地监控过程中发生的微小偏移,但是因为它需要存储大量统计量且计算时间较长,所以在计算机中实施起来比较困难.为了解决这一问题,介绍了将控制点方法论应用于V-mask累积和控制图这一方法,并通过实例来进一步说明.结果表明,与控制点方法论结合的控制图减少了存储量,缩短了计算时间,而且将在顾客满意度控制中得到广泛应用.     

A Neural Network Method for Modelling the Parameters of a CUSUM Chart

The cumulative sum (CUSUM) chart is widely employed in quality control to monitor a process or to evaluate historic data. CUSUM charts are designed to exhibit acceptable average run lengths both when the process is in and out of control. This paper introduces a functional technique for generating the parameters h and k for such a chart that will have specified average run lengths. It employs the method of artificial neural networks to derive the appropriate coefficients. An EXCEL spreadsheet to assist computing the parameters is presented.     

A Generalized Likelihood Ratio Control Chart for Monitoring the Process Mean Subject to Linear Drifts

This article considers the problem of monitoring a normally distributed process variable when a special cause may produce a time‐varying linear drift in the mean. The design and application of a generalized likelihood ratio (GLR) control chart for drift detection are evaluated. The GLR drift chart does not require specification of any tuning parameters by the practitioner and has the advantage that, at the time of the signal, estimates of both the change point and the drift size are immediately available. An equation to accurately approximate the control limit is provided. The performance of the GLR drift chart is compared with that of other control charts such as a standard cumulative sum chart and a cumulative score chart designed for drift detection. We also compare the GLR chart designed for drift detection with the GLR chart designed for sustained shift detection because both of them require only a control limit to be specified. In terms of the expected time for detection and in terms of the bias and mean squared error of the change‐point estimators, the GLR drift chart has better performance for a wide range of drift rates relative to the GLR shift chart when the out‐of‐control process is truly a linear drift. Copyright © 2012 John Wiley & Sons, Ltd.     

An Evaluation of the Crosier's CUSUM Control Chart with Estimated Parameters

We evaluate the performance of the Crosier's cumulative sum (C‐CUSUM) control chart when the probability distribution parameters of the underlying quality characteristic are estimated from Phase I data. Because the average run length (ARL) under estimated parameters is a random variable, we study the estimation effect on the chart performance in terms of the expected value of the average run length (AARL) and the standard deviation of the average run length (SDARL). Previous evaluations of this control chart were conducted while assuming known process parameters. Using the Markov chain and simulation approaches, we evaluate the in‐control performance of the chart and provide some quantiles for its in‐control ARL distribution under estimated parameters. We also compare the performance of the C‐CUSUM chart to that of the ordinary CUSUM (O‐CUSUM) chart when the process parameters are unknown. Our results show that large number of Phase I samples are required to achieve a quite reasonable performance. Additionally, the performance of the C‐CUSUM chart is found to be superior to that of the O‐CUSUM chart. Finally, we recommend the use of a recently proposed bootstrap procedure in designing the C‐CUSUM chart to guarantee, at a certain probability, that the in‐control ARL will be of at least the desired value using the available amount of Phase I data. Copyright © 2015 John Wiley & Sons, Ltd.     

A new dual CUSUM mean chart

The conventional cumulative sum (CUSUM) chart is usually designed based on a known shift size. In usual practice, shift size is often unknown and can be assumed to vary within an interval. With such a range of shift size, the dual CUSUM (DCUSUM) chart provides more sensitivity than the CUSUM chart. In this paper, we propose dual Crosier CUSUM (DCCUSUM) charts with and without fast initial response features to efficiently monitor the infrequent changes in the mean of a normally distributed process. Monte Carlo simulations are used to compute the run length characteristics of one‐sided and two‐sided DCCUSUM charts. These run length characteristics are compared with those of the CUSUM, Crosier CUSUM, Shewhart‐CUSUM, and DCUSUM charts in terms of the integral relative average run length. It turns out that the proposed chart shows better performance when detecting a range of mean shift sizes. A real dataset is considered to illustrate the implementation of existing and proposed charts.     

Computing the Percentage Points of the Run-Length Distributions of Multivariate CUSUM Control Charts

Multivariate CUSUM control charts are often used instead of the standard Hotelling's control charts in many practical problems when detection of small shifts in the process mean is important. However, design of multivariate CUSUM control charts are usually based on the average run length (ARL). In this work, we will compute the percentage points of the run-length distributions of two multivariate CUSUM control charts. It will be shown that interpretations based on ARL can be misleading since the in-control run-length distribution of a multivariate CUSUM is highly skewed. On the other hand, the percentage points of the run-length distribution provide additional information such as the median run length, early false out-of-control signals, and the skewness of the run-length distribution for a particular scheme. These extra information might provide quality control engineers further knowledge of a particular multivariate CUSUM control chart scheme.     

New Synthetic EWMA and Synthetic CUSUM Control Charts for Monitoring the Process Mean

Exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are potentially powerful statistical process monitoring tools because of their excellent speed in detecting small to moderate persistent process shifts. Recently, synthetic EWMA (SynEWMA) and synthetic CUSUM (SynCUSUM) control charts have been proposed based on simple random sampling (SRS) by integrating the EWMA and CUSUM control charts with the conforming run length control chart, respectively. These synthetic control charts provide overall superior detection over a range of mean shift sizes. In this article, we propose new SynEWMA and SynCUSUM control charts based on ranked set sampling (RSS) and median RSS (MRSS) schemes, named SynEWMA‐RSS and SynEWMA‐MRSS charts, respectively, for monitoring the process mean. Extensive Monte Carlo simulations are used to estimate the run length characteristics of the proposed control charts. The run length performances of these control charts are compared with their existing powerful counterparts based on SRS, RSS and MRSS schemes. It turns out that the proposed charts perform uniformly better than the Shewhart, optimal synthetic, optimal EWMA, optimal CUSUM, near‐optimal SynEWMA, near‐optimal SynCUSUM control charts based on SRS, and combined Shewhart‐EWMA control charts based on RSS and MRSS schemes. A similar trend is observed when constructing the proposed control charts based on imperfect RSS schemes. An application to a real data is also provided to demonstrate the implementations of the proposed SynEWMA and SynCUSUM control charts. Copyright © 2014 John Wiley & Sons, Ltd.