单值移动极差控制图在脱旨浓度控制过程中的应用

本文简要介绍了控制图的作用和种类,通过对脱脂浓度参数一游离碱浓度作单值移动极差控制图进行分析监控,表明单值移动极差控制阻对异常反应灵敏,能够很好地起到预防控制作用。     

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。     

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。     

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。     

单值-移动均值控制图在热处理工序中的应用

在研究统计过程控制理论的基础上,提出了一种全新的正态分布控制图——单值一移动均值控制图。论述了它的构造原理和控制线计算公式,提出了判异准则,介绍了此控制图在热处理工序的应用状况,效果显著。     

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.     

Q控制图灵敏性分析

对过程均值未知,过程标准偏差已知情况下的单值Q控制图与均值Q控制图检测过程均值一次偏移的灵敏性进行了分析.结果表明:在过程初始阶段,单值Q图的灵敏性要高于均值Q控制图.在过程初始阶段,建立单值Q控制图监测过程波动,累积一定数量的样本后,建立均值Q控制图监视过程波动.两种Q控制图互为补充,共同用于监视过程波动更有效.     

平均值-极差控制图在音速喷嘴式气体流量标准装置测量过程中的应用

音速喷嘴式气体流量标准装置是气体流量重要的计量标准,其测量过程的情况直接反映了装置的准确性、重复性、稳定性。使用控制图可以记录装置的测量过程,并由此判断其测量过程是否处于受控状态。本文介绍了平均值-极差控制图在音速喷嘴式气体流量标准装置测量过程中的应用。     

浅谈X--S计量控制图在质量管理中的应用

统计过程控制的基本工具是控制图，控制图可用于何处?原则上讲，对任何过程．凡需要对质量进行控制的场合都可以使用控制图。但这里还要求，对于所确定的控制对象一般能够定量计量，这样才能使用计量控制图。计量控制图的理论依据是正态分布，它敏感性较强，容易发现过程中的变化，有助于查明原因，在分析和控制生产过程的稳定性，预防不合格品的产生，保证和提高产品质量方面都能起到重要作用。     

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。     

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.     

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.     

同类过程控制图的一个实例

讨论了多台机器生产相同产品时应用控制图的问题。按同类过程的思路,采取了应用箱子图帮助建立共同管理用控制图。这样不但可避免作分层控制图,还可在提高产品质量时抑制多台机器生产过程出现差别。     

Adaptive nonparametric CUSUM scheme for detecting unknown shifts in location

In this paper, we provide a sequential rank-based adaptive nonparametric cumulative sum control chart for detecting a range of shifts in the location parameter. This chart is a self-starting scheme, and thus can be used to monitor processes at the start-up stages rather than having to wait for the accumulation of sufficiently large calibration samples. It does not require any prior knowledge of the underlying distribution. The choice of the chart parameters is studied and a simulation study demonstrates that the proposed control chart not only performs robustly for different distributions, but also efficiently detects various magnitudes of shifts. An illustrative example is also given to introduce the implementation of this proposed control chart.     

A Note on the Lower-Sided Synthetic Chart for Exponentials

The synthetic control chart for exponential data is discussed and an expression is derived for its average run length, as well as its design parameters. The synthetic control chart for exponentials is shown analytically to be a two-in-a-row rule. This chart is compared with the Shewhart chart for individuals and with the worst-case, lower-sided exponential EWMA and CUSUM charts. While the synthetic control chart for exponentials outperforms the Shewhart chart for individuals, the EWMA and CUSUM charts are shown to be far superior in detecting decreases in the exponential mean.     

A distribution-free tabular CUSUM chart for autocorrelated data

A distribution-free tabular CUSUM chart called DFTC is designed to detect shifts in the mean of an autocorrelated process. The chart's Average Run Length (ARL) is approximated by generalizing Siegmund's ARL approximation for the conventional tabular CUSUM chart based on independent and identically distributed normal observations. Control limits for DFTC are computed from the generalized ARL approximation. Also discussed are the choice of reference value and the use of batch means to handle highly correlated processes. The performance of DFTC compared favorably with that of other distribution-free procedures in stationary test processes having various types of autocorrelation functions as well as normal or nonnormal marginals.