生产线监控与管理系统的设计

介绍在Labview的开发环境下,装配检测生产线监控与管理系统的开发方法。该系统实现了检测数据的人机界面显示,查询打印,统计过程控制(SPC)以及每月的不合格工件计数。统计过程控制(SPC)实现了对生产线的实时监控及质量管理,其他三个软件模块实现了对生产线的自动化生产管理。这种模块化的生产线管理系统对设计同类软件具有借鉴意义。     

spc技术在软件过程监控中的应用探讨

统计过程控制(SPC)是质量管理的重要内容。该文在介绍软件过程质量管理的相关理论基础上,主要探讨了SPC特别是控制图在软件过程监控中的应用,并简单讨论软件过程改进的SPC方法。     

统计过程控制(SPC)及其在一汽大柴的应用

统计过程控制（SPC）是质量管理的重要内容。一汽大柴将SPC理论与计算机技术相结合，开发了SPC分析与管理系统。文中介绍了SPC理论及一汽大柴SPC分析与管理系统的网络环境、数据采集和软件功能。     

应用统计过程控制监控软件测试过程

统计过程控制(SPC)是通过使用控制图来制定过程决策和预测过程行为的一种质量控制方法.SPC的方法用于软件过程,可以通过描述过程行为来监控过程的稳定性.讨论了将SPC应用于软件测试过程,针对测试过程中所度量的不同分布形式的数据而采用不同计算方式应用SPC的控制图,然后根据控制图判断测试过程是否稳定,并分析可能存在的可归属原因.     

基于电子标签的SPC过程控制分析

SPC统计过程控制技术正在成为企业质量控制的重要分析工具.通过对某大型制造业生产流程的研究,找出了质量管理中存在的问题,提出了基于电子标签的SPC统计过程控制系统,分析了系统的结构和电子标签系统和SPC之间的数据关系,描述了数据采集过程,再造了业务流程.实际应用证明了本系统的有效性.     

基于SPC的过程监控软件的实现

简要介绍了统计过程控制(SPC)的原理和基于SPC过程监控软件的结构设计和具体实现方法,以及用SPC监控过程的方法.并给出了相应的实例.开发了基于SPC的监控过程的软件系统,用软件可以实时分析生产过程情况,对异常情况提前预测,并给出合理化建议,从而控制生产过程的波动情况,使过程更稳定、直观、易控.     

SPC研究及其在芯片制造工艺中的应用

统计过程控制(SPC)技术是集生产技术与科学管理于一体的现代工艺质量管理技术。在企业生产过程中,SPC是生产过程控制的有效手段和工具。SPC作为一种过程控制方法,运用数理统计概率论的原理,应用统计方法对过程中的各个阶段进行监控,可及时发现生产过程中的异常情况。     

统计过程控制(SPC)及其应用研究

统计过程控制(SPC)是质量管理的重要内容。该文介绍了SPC理论、计算机实现以及在企业中的应用。     

轴承统计过程控制的研究及软件编制

统计过程控制（SPC）是指应用统计分析技术对生产过程进行实时监控,区分出生产过程中产品质量的异常波动,以便管理人员及时采取措施,消除异常,达到提高和控制质量的目的。针对SPC的实际应用研究,SPC的软件编制阐述了统计过程控制原理,提出相关数学模型,对其模拟软件的设计进行了研究分析,并对相关的内容作了进一步的讨论。在理论知识的基础上采用高级编程语言进行软件编制,实现具有一定实用性的软件应用系统。     

零件加工中LabVIEW与质量控制技术的应用

针对零件加工公差精度的控制要求,提出了滑动轴承垫圈厚度的测量系统,以及对生产过程的加工质量采用统计过程控制(SPC)分析模块的设计,设计包括数据采集和数据SPC分析系统。数据采集基于LabVIEW软件设计的数据采集模块以及数据实时显示与存储模块;数据SPC分析模块采用Excel中6SQ统计插件进行SPC分析,并绘制均值极差控制图。通过对加工质量进行实例分析,论证了质量控制在产品加工过程中应用的可行性。     

基于统计过程控制技术的钢卷质量控制

统计过程控制（Statistical Process Control, SPC）是生产过程质量控制的重要工具,包括过程监控、过程诊断与改进等一系列活动。详细阐述了此技术在钢卷质量控制方面的应用流程,并通过实例重点介绍了控制图在钢卷质量分析与控制中的应用。系统采集钢卷工艺数据,选择控制图对工艺参数进行监控,计算和分析工序的工序能力指数。发现工序失控时,分析原因并及时采取纠正预防措施,保证工艺的一致性和稳定性,提高工艺成品率。     

A review of neural networks for statistical process control

This paper aims to take stock of the recent research literature on application of Neural Networks (NNs) to the analysis of Shewhart's traditional Statistical Process Control (SPC) charts. First appearing in the late 1980s, most of the literature claims success, great or small, in applying NNs for SPC (NNSPC). These efforts are viewed in this paper as useful steps towards automatic on-line SPC for continuous improvement of quality and for real-time manufacturing process control. A standard NN approach that can parallel the universality of the traditional Shewhart charts has not yet been developed or adopted, although knowledge in this area is rapidly increasing. This paper attempts to provide a practical insight into the issues involved in application of NNs to SPC with the hope of advancing the use of NN techniques and facilitating their adoption as a new and useful aspect of SPC. First, a brief review of control chart analysis prior to the introduction of NN technology is presented. This is followed by an examination and classification of the NNSPC existing literature. Next, an extensive discussion of implementation issues with reference to significant research papers is presented. Finally, after summarising the survey, a set of general guidelines for future applications of NNs to SPC is outlined.     

Data mining model-based control charts for multivariate and autocorrelated processes

Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of multivariate and autocorrelated data found in modern systems. As the limitations of SPC methodology become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. In the present study we attempted to integrate state-of-the-art data mining algorithms with SPC techniques to achieve efficient monitoring in multivariate and autocorrelated processes. The data mining algorithms include artificial neural networks, support vector regression, and multivariate adaptive regression splines. The residuals of data mining models were utilized to construct multivariate cumulative sum control charts to monitor the process mean. Simulation results from various scenarios indicated that data mining model-based control charts performs better than traditional time-series model-based control charts.     

基于反馈调整的自相关过程质量损失分析

传统的SPC要求过程观测值统计独立,而实际过程大多具有自相关性,不满足统计独立的要求。在SPC与EPC的整合框架下,分析了自相关过程的质量损失。根据模型的自相关系数(ACF)和偏相关系数(PACF)以及选择迭代算法识别模型参数;采用MMSE反馈控制器对自相关过程进行调整,并结合过程能力,对自相关过程的质量损失进行分析。结果表明,在MMSE反馈调整后,自相关过程质量损失才符合实际情况。     

Identification of process disturbance using SPC/EPC and neural networks

Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks' parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully.     

A case-based reasoning system development for statistical process control: Case representation and retrieval

Statistical process control (SPC) is a sub-area of statistical quality control. Considering the successful results of the SPC applications in various manufacturing and service industries, this field has attracted a large number of experts. Despite the development of knowledge in this field, it is hard to find a comprehensive perspective or model covering such a broad area and most studies related to SPC have focused only on a limited part of this knowledge area. According to many implemented cases in statistical process control, case-based reasoning (CBR) systems have been used in this study for developing of a knowledge-based system (KBS) for SPC to organize this knowledge area. Case representation and retrieval play an important role to implement a CBR system. Thus, a format for representing cases of SPC and the similarity measures for case retrieval are proposed in this paper.     

统计过程监测中低阶EPC控制器分析与设计

观测值相互独立并服从正态分布是应用统计过程控制(SPC)的基本前提,然而由于某些不可消除因素,实际过程的输出观测值常常是自相关的。采用SPC与EPC整合,消除过程自相关,实现对自相关过程的监控。将状态空间分析法引入到EPC控制器的设计中,通过极点配置方法来分析EPC控制器的性能,研究平均运行链长(ARL)与极点配置的关系。最后对均值发生阶跃型故障的自相关ARMA(1,1)过程进行仿真实验,得到EPC控制器极点的较优配置范围。仿真结果亦证明了该方法的可行性和有效性。