过程均值偏移随机的EWMA控制图优化设计

针对过程均值偏移随机的情况,提出一种统计经济最优的指数加权移动平均控制图优化设计方法。该方法将过程受控、失控未检出、失控被检出并进行恢复这三个阶段定义为一个周期,分析了三个阶段的平均时长及质量成本构成,通过计算产品质量特性超出规格界限的概率量化缺陷产品所造成的质量损失,以单位时间内期望成本最小为目标建立指数加权移动平均控制图优化模型并设计了遗传算法,优化了样本容量、采样间隔、平滑界限和控制界限等参数。通过与休哈特均值控制图、传统指数加权移动平均控制图等进行对比验证了该模型的优越性。     

累积和控制图与传统控制图同时监控过程均值

质量管理中广泛使用的休哈特控制图，对异常原因引起的均值大的偏移比较有效，但对于小的持续的偏移却不敏感；累积和控制图对小的持续性偏移有效。但对瞬间偏移不敏感。在过程质量的监控中，同时采用这两种控制图来进行监控，包容了两种控制图的优点，弥补了彼此的不足。在一定条件下，提出了一种新的控制图——累积和和休哈特混合控制图，效果明显优于累积和控制图或休哈特控制图。     

监控均值标准差的可变抽样区间EWMA图经济设计

为了在提高过程监控效率的同时降低过程监控费用,针对同时监控过程均值和标准差变化的情形,研究可变抽样区间的指数加权移动平均控制图的经济设计问题.首先建立可变抽样区间,同时监控均值标准差指数加权移动平均控制图；其次对其进行经济设计,使单位时间期望费用函数最小,以确定控制图参数的最优值；然后利用遗传算法来寻找经济模型的参数最优解；最后对可变抽样区间同时监控均值标准差指数加权移动平均控制图的经济模型进行灵敏度分析和最优性分析.通过最优性分析得出结论:基于经济模型设计的可变抽样区间指数加权移动平均控制图比传统的控制图具有较小的费用.     

竞争型人工神经网络的统计过程控制模式识别

统计过程控制(SPC)在现代制造和服务行业中是最常应用的性能改进工具之一,休哈特控制图作为SPC的基本方法,在质量管理领域得到了充分的应用,但是控制图的识别一直比较困难.为此,将神经网络引入到休哈特控制图中,用神经网络的方法对质量控制中控制图进行了识别,并建立了以竞争型人工神经网络为基础的新的神经网络,判断出控制图中的基本失效模式.     

小批量过程多变异控制图技术研究

针对小批量过程的主要控制图技术中目标图、比例图和标准变换图,归纳出小批量过程控制的同分布原理。考虑到不同小批量之间的差异,以目标均值控制图为例,对控制图进行改进,给出多变异目标均值控制图的建立步骤。通过一个例子发现,该方法既实现了小批量过程多变异的控制,又降低了控制图虚发警报的概率。     

电机转轴加工质量检测与控制系统的开发

电机转轴加工质量检测与控制系统,可实现电机转轴轴承档直径的自动检测,然后通过与计算机连接,实现数据的实时采集,最后利用休哈特控制图及工序能力指数等统计过程控制工具,实现电机转轴加工工序的质量控制。     

MEWMA控制图运行链长分析及实例研究

多元指数加权移动平均（MEWMA）控制图常用来监测多元质量过程中存在的小偏移，对其性能评价时多采用平均运行链长。在马尔可夫链的MEWMA控制图平均运行链长计算方法的基础上，分析了MEWMA控制图运行链长的概率分布及累积概率分布。基于MATLAB计算平台，编制了相应的计算程序，给出了MEWMA控制图运行链长的概率分布曲线及累积概率曲线。提出了MEWMA控制图参数优化方法，对电子产品装配中的表面贴片工艺过程的焊点质量控制进行了研究。     

电机振动的质量检测与控制系统

设计了电机振动速度的在线自动检测与质量控制系统。通过电涡流传感器对工件的振动速度进行采集,经过以计算机为核心的控制系统的计算,利用休哈特控制图和过程能力指数等统计过程控制工具,通过人机界面实时显现,并根据闽值报警,实现对电机振动速度的质量控制。     

方差监测的自适应累积和车身质量控制图研究

针对车身制造质量检测工作量大、数据处理方式简单等特点,提出一种基于方差的改进累积和控制图（CUSUM）方法,用于监测车身制造质量的方差波动。其基本思想是对控制图参数k动态更新和迭代,并与方差波动量相联系,以便实时监测车身焊接尺寸过程方差的微小波动。通过对控制图的平均运行链长进行分析和实际案例研究,并与常规累积和控制图和指数加权移动平均控制图作对比,表明该方法对过程方差微小变异更为有效和敏感。     

累积和控制图在摩托车车灯闪光器生产过程中的应用

摩托车闪光器的闪烁频率检测往往使用基于3σ原理的休哈特(Shewhart)控制图,但是经典的休哈特控制图对频率发生的小漂移不够敏感。当小漂移发生时,会导致闪光器的闪烁频率调整不及时,采用累积和控制图(CUSUM控制图)来监控闪光器的闪烁频率,能及时监控闪光器的闪烁频率发生的中小漂移,提高了控制图的效率,在保证产品质量的同时也降低了生产成本。     

Generally weighted moving average control chart for monitoring process variability

Most applications of the EWMA control chart for monitoring processes depend on detecting shifts in the process mean. The problem of detecting an increase in process variability, which can also strongly affect the quality of products, is perhaps more important. When a process moves from the pilot phase to the production phase, the mean may not shift but the variation will probably increase because new sources of variation are introduced, including new people and materials. A simulation is performed to evaluate the ARL to false alarm and to monitor the change in the process variability of the EWMA control chart and the GWMA control chart. An extensive comparison reveals that the GWMA control chart is more sensitive than the EWMA control chart in monitoring the variance of a process. The results of this study can be applied to monitor the process variability in automated industries.     

Monitoring process variability using exponentially weighted moving sample variance control charts

Exponentially weighted moving average (EWMA) control charts are regarded as one of the most convenient tools in detecting small process shifts. Although EWMA control charts have been extensively used to monitor the mean of quality characteristics, there are few studies concentrating on the monitoring of process variability by using weighted moving control charts. In this paper, we propose an exponentially weighted moving sample variance (EWMSV) control chart for monitoring process variability when the sample size is equal to 1. The results are compared numerically with other similar methods using the average run length (ARL). Through an example, the practical considerations are presented to implement EWMSV control charts.     

A new link function in GLM-based control charts to improve monitoring of two-stage processes with Poisson response

In this paper, a new procedure is developed to monitor a two-stage process with a second stage Poisson quality characteristic. In the proposed method, log and square root link functions are first combined to introduce a new link function that establishes a relationship between the Poisson variable of the second stage and the quality characteristic of the first stage. Then, the standardized residual statistic, which is independent of the quality characteristic in the previous stage and follows approximately standardized normal distribution, is computed based on the proposed link function. Then, Shewhart and exponentially weighted moving average (EWMA) cause-selecting charts are utilized to monitor standardized residuals. Finally, two examples and a case study with a Poisson response variable are investigated, and the performance of the charts is evaluated by using average run length (ARL) criterion in comparison with the best literature method.     

DAMAGE DETECTION OF THE Z24 BRIDGE USING CONTROL CHARTS

Structural health monitoring of the Z24 Bridge in Switzerland was studied using the measurement data from three damage configurations. Changes in the modal parameters were used to detect possible damage to the structure. The identification of the modal parameters from the response data was automated using the stochastic subspace identification technique and the stabilisation diagram. Damage detection was performed using control charts, one of the primary techniques of statistical process control. An advantage of control charts is that they can be automated for on-line structural health monitoring. Univariate and multivariate Shewhart, x, CUSUM, and EWMA control charts were studied with different features including natural frequencies, mode shapes, and damping ratios. The sensitivity of the control chart to damage was substantially increased by further dimensionality reduction applying the principal component analysis.     

Weighted-loss-function control charts

This article proposes a single weighted-loss-function chart (WL chart) for monitoring the process mean and variance simultaneously in statistical process control (SPC). The weights of the losses due to mean shift and variance shift in the loss function are balanced through a weighting factor λ, so that the WL chart is considerably more effective than the unadjusted loss function chart, the joint &S charts and many other charts as well. The improvement in detection effectiveness will directly lead to the reduction of a number of defects in the manufacturing process when out-of-control cases occur. Moreover, the WL chart provides a platform for the further development of the Cusum chart, runs rule, or adaptive chart for monitoring both process mean and variance.     

面向多品种小批量制造过程的NAD-EWMA控制图多目标优化设计方法

针对面向多品种小批量制造过程设计质量控制图面临的样本数量少、分布不确定问题,提出一种基于非参数、自适应、动态EWMA控制图的多目标优化设计方法。基于非参数统计理论与自适应控制的思想,构建与样本数据分布无关的控制图统计量,并设计一种基于聚类距离的动态抽样方法实现样本抽样;在此基础上,考虑统计性、经济性建立控制图多目标优化设计模型,基于改进人工鱼群算法与云清晰综合评价方法实现对模型求解,进而构建面向多品种小批量制的非参数自适应动态EWMA控制图。最后,以航天复杂构件制造过程为例,对制造过程进行动态监控。结果表明,所提方法能够快速监控到质量异常,监控性能高,验证了该方法的有效性和可行性,为多品种小批量制造过程实际的质量监控提供一种有效的途径。     

Determination of Optimal Design Parameters of Moving Average Control Charts

The economic design of a control chart can reduce the cost of process control and promote the competitiveness of an enterprise. This type of design has been studied for over four decades since Duncan’s pioneering work in 1956. Few studies, however, have focused attention on the economic design of a moving average (MA) control chart. Montgomery pointed out that an MA control chart is more effective than a Shewhart control chart for detecting small process shifts and is considered by some to be simpler to implement than the CUSUM. This study proposes an economic model of an MA control chart with multiple assignable causes. This economic model is then applied to a realistic case from the chemical industry, and sensitivity analysis shows that the magnitude of shift, rate of occurrence of assignable causes and penalty cost, when the process is out of control, should be more carefully estimated when conducting an economic analysis.. ID="A1"Correspondance and offprint requests to: Yun-Shiow Chen, Department of Industrial Engineering and Management, Yuan-Ze University, 135 Yuan-Tung Road, Nei-Li 32026, Taoyuan Shian, Taiwan. E-mail:ieyschen@saturn.yzu.edu.tw     

An EWMA t chart with variable sampling intervals for monitoring the process mean

This paper proposes a variable sampling interval (VSI) version of the fixed sampling interval (FSI) exponentially weighted moving average (EWMA) t chart developed by Zhang et al. for monitoring the changes in the process mean. An optimal design strategy based on the average time to signal (ATS) is presented. We determine the optimal parameters for the VSI EWMA t chart using a Markov chain approach so that the chart has the desired robustness property against errors in estimating the process standard deviation or changing standard deviation. Also, we explain how the various parameters of this VSI EWMA t chart can be computed and how the use of the VSI feature improves the statistical efficiency of the FSI EWMA t and FSI EWMA X-bar charts in terms of out-of-control ATS performances. Comparisons with the FSI EWMA t and FSI EWMA X-bar charts are performed.