小批量产品生产过程统计质量控制模型研究

在分析研究现有小批量统计质量控制相关理论的基础上,基于贝叶斯原理,提出了一种综合解决小批量生产过程质量控制的建模方法,此方法在保证第一类错误较小的前提下有效地减小第二类错误的发生,即在误发警报率尽量小的前提下减小漏发警报的发生。     

面向多品种小批量生产的SPC研究

分析了传统控制图应用于多品种小批量生产环境下存在的缺陷,从而提出了一种新的面向多品种小批量生产的统计过程质量控制图--动态控制限控制图(X-R图),它采用动态的控制界限方法建立控制图,极大地避免了"虚发警报"的发生.并结合实例验证了该控制图的有效性.     

基于小批量生产的统计质量控制

以多品种小批量为主的高智能、高柔性自动化生产加工系统是未来的主导生产方式。传统的控制图应用于小批量生产质量控制时，存在着“误发警报”概率增加的问题。新的基于小样本的控制图模型，其控制界限可靠稳定，不受样本组数量的限制，较好地解决了小批量产品生产质量控制中频繁出现错误告警现象的问题。     

面向先进制造系统的小批量统计质量控制的研究

分析了传统控制图应用于先进制造系统面临的问题，提出一种新的基于小批量生产环境的质量控制图，它采用了可变的控制界限，不管样本数量多少，都能使误发警报概率保持较小的定值，从而避免了错误的频繁报警，无谓的停机检查和多次调整，提高了控制图对生产工序的监控效率。     

基于小批量制造过程的动态质量控制限及其简便计算方法

讨论了一种基于t分布动态控制限的小批量生产质量控制方法，并给出了该控制限的简便计算过程。控制方法通过分析抽样样本数量与控制图虚发警报概率之间的函数对应关系，得到一组能使虚发警报概率保持相对稳定的动态控制限，建立了基于t分布的控制界限值随样本数量变化的数学模型，并利用上侧分位数的初等数学表达式近似表示t分布，从而简化计算过程。实际计算结果表明，当样本组数小于5时，控制方法的计算误差仅为正态逼近法的20％。控制方法形式简单，不需使用正态分布函数，适合小批量生产过程的质量控制。     

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。     

小批量生产条件下的统计过程控制研究

从小批量生产的定义入手，分析传统的休哈特控制图应用在小批量生产条件下存在的问题及局限性。对目前小批量生产质量控制的方法进行简要分析。在此基础上，提出一种适合于小批量生产质量控制的新思路，并用实际数据对提出的方法与传统的休哈特控制图方法进行了对比，说明这种新思路应用在小批量生产过程中的可行性和有效性。     

基于小批量生产的统计过程质量控制研究

分析了在小批量生产环境下实施统计过程质量控制存在的问题,对过程质量特征数据服从正态分布的生产过程,给出了基于概率积分变换理论的控制过程均值、过程方差的标准化控制图的方法,实现了对小批量生产过程的实时控制.     

运用贝叶斯分析理论研究小批量生产质量

在当今全球经济一体化的环境下,小批量生产已成为企业主要的生产模式,这给传统统计质量控制理论提出了一个新课题。运用贝叶斯分析理论建立小批量生产质量控制模型,来提高小批量生产的质量。     

基于小批量的统计过程控制研究

分析了传统的休哈特控制图应用在小批量生产条件下存在的问题及其局限性。在此基础上提出了一种适合于小批量生产质量控制的新思路，并与传统的休哈特控制图方法进行了对比。证明了这种新思路应用在小批量生产过程中的可行性和有效性。     

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。     

Evaluation-based closed-loop errors using principal component analysis and self-organisation map with an application to a pickling process

Closed-loop control is a basic technology in control engineering. Its role is to avoid the tracking error between set points and real variables. The evaluation of plant performance can be based on multivariate statistical process control connected to closed-loop errors behaviour. Due to its practicality, this approach has found many applications in several industries. This paper suggests a combined use of principal component analysis (PCA) and self-organisation map (SOM) algorithms to evaluate the process on the basis of closed-loop errors dynamic. Generally, it is possible to evaluate a product quality in the basis of the dynamic changes of the closed-loop control errors. In this paper, a new method based on the analysis of the control errors is proposed; it is carried out by a combined use of the PCA-SOM algorithm. Comparatively to the conventional PCA method, this new technique is characterised by the performant indexes that give an accurate evaluation of the process variability and its impact on the product quality. As shown in the different simulation results, the proposed approach gives a global evaluation and improves considerably the performance of computed indexes used for the evaluation of the controlled process.     

Detection of Gross Measurement Errors Using the Grey System Method

A novel method using the grey system to detect the gross errors involved in a measurement process is proposed. Although the gross errors should be avoided completely, they can occur and on many occasions are not apparent. The grey system theory is used to characterise the geometric aspects in the measurement. Theoretical analysis of the principle of gross error detection is presented and a detection criterion is proposed. An experimental test shows that the proposed method compares favourably with statistical methods which are difficult to implement when the distribution of the data is unknown and the sample size is small.     

Monotonic change point estimation in the mean vector of a multivariate normal process

When a control chart sounds the alarm that the process is out of control (OC), the process will be paused and specialists will start the procedure of finding the root cause(s) that made the process out of control. Knowing the time of change will substantially aid the process engineer to figure out the assignable causes and solve the problem sooner, so the time, energy, and costs spent to implement corrective actions will be considerably reduced. Maximum likelihood estimator (MLE) as one of the statistical technique is frequently used for estimating the change point time. In this paper, an MLE is derived to estimate the time of first change in the mean vector of a multivariate normal process when the type of change is monotonic. The performance of the proposed change point estimator is evaluated in terms of accuracy and precision in comparison with the change point estimators developed under the assumptions of a step shift and drift. Finally, a numerical example is presented to show the application of the proposed change point estimator.     

小批量生产模式下工序质量统计控制研究

提出了加工误差等效变换原理和计算方法,在此基础上建立了一种等效的统计过程控制模型,为小批量加工模式的统计过程控制提供了一种可行的方法.     

基于PROFIBUS - DP通信的污泥脱水管控一体化系统

介绍基于PROFIBUS - DP通信的污泥脱水管理和控制一体化系统,实现污水处理的自动化过程检测和控制.该系统利用中小型PLC、HMI、PROFIBUS - DP现场总线等成熟技术,组成了一个系统信息集中,主体设备分散控制的由现场控制级、中间控制级和生产管理三层控制网组成的污泥脱水管控一体化系统.此套系统投入已来最大限度地按照生产要求,合理利用公共辅助设备及主体设备,节约了投资及维护支出.同时,出于生产管理方面的需要,系统提供了大量的生产运行实时数据及记录,便于统计分析.经实践使用,这套自动化管理和控制一体化系统在污水处理中达到了预期的管理和控制效果.     

Change point estimation in phase I monitoring of logistic regression profile

In statistical process control, an important issue in phase I is to identify the time of a change in process parameters. Control charts monitor the process over time, but the time an alarm is signaled by a control chart is not necessarily the real time of change in the process. Finding the real time of change, called as change point, is important because it leads to saving cost and time in detecting the assignable cause. Recently, profile monitoring in which a response variable and one or more explanatory variables are modeled by a regression function is attracted by many researchers. One type of profiles considered in the literature is a logistic profile where the distribution of the response variable is binary. In this paper, we develop two methods including likelihood ratio test and clustering to estimate the real time of a step change in phase I monitoring of the logistic profiles. The performance of the proposed methods is evaluated and compared through simulation studies. The results show the efficiency of both estimator methods. A real case is also studied to show the applicability of the proposed methods in practice.