基于统计过程控制的软件开发过程研究与应用

为了将统计过程控制(statistical process control,SPC)应用于软件开发过程,根据项目实例和经验分析了软件开发过程的特点及应用SPC的难点,通过在测试阶段进行SPC控制图检测,分析得出以过程为中心的重要性,提出了一种利用前摄活动改进软件过程的方法.讨论了自我导向能力在改善软件过程中的意义和价值,给出了将SPC应用于软件开发过程的方法、步骤及注意事项,通过同行审查实例表明了该方法的可行性和有效性.     

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。     

基于SPC的软件过程度量技术的研究

本文将统计过程控制原理SPC应用于软件过程度量。在介绍SPC原理的基础上,讨论了休哈特控制图的构成和分析方法,结合实例,深入分析了SPC在软件过程度量中的应用。     

基于ART1神经网络的统计过程控制系统

统计过程控制(SPC)在改进过程水品、提高产品质量方面作出了巨大贡献.本文讨论了一种基于自适应谐振理论(ART)神经网络的SPC系统.与一般SPC系统相比,本系统不仅可以在线检测过程异常,对各种控制图异常模式还具有实时学习、在线识别功能.同时,本系统对过程的分析,无需如常规控制图一样,建立在正态假设的前提下,因此应用更方便、范围更广泛.作为一种新的SPC工具,ART1神经网络为改进控制图的应用提供了一种新的可能.     

AHP和SPC技术在量化过程管理中的应用

为了实施能力成熟度模型集成(CMMI)高成熟度实践,研究了层次分析法(AHP)和统计过程控制(SPC)两种重要的定量分析技术在量化过程管理中的应用.应用AHP建立从量化的目标到过程的映射关系,为过程进行优先级排序,辅助选择待分析的标准过程和项目过程.应用SPC建立组织过程性能基线,并定量地监控项目过程能力和过程稳定性.通过实例表明了应用AHP和SPC辅助量化过程管理的实施方法,最后分析了在应用AHP和SPC时要注意的问题.     

回顾与展望——组态软件的发展

组态软件是工业自动化软件的重要分支,所谓组态就是利用工控软件中提供的工具和方法来完成工程中某一具体任务的过程,而这个软件就是组态软件。组态软件主要具备以下功能及特征：工业过程动态可视化、数据采集和管理、过程监控与报警、生成报表、为其他企业级程序提供数据、简单控制、批次处理、SPC过程质量控制、符合IEC61131-3标准等。     

基于SPC和神经网络的卷烟制丝生产质量监控方法研究

为解决卷烟制丝生产过程中现有SPC监控方法存在的问题,提出了基于SPC和BP神经网络的质量监控方法.首先在传统控制图的基础上,提出了适合在线监控的移动窗口式控制图,然后分别建立了用于控制图模式识别和质量缺陷原因诊断的两个神经网络模型,最后通过松散回潮工序中出口物料含水率的质量监控实例,证明了该质量监控方法的有效性.     

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.     

与时俱进的自动化组态软件

我们今天讨论的自动化组态软件是工业自动化软件的重要分支,也是应用非常广泛的自动化软件产品之一。所谓组态,就是利用工控软件中提供的工具和方法来完成工程中某一具体任务的过程,而这个软件就是组态软件。组态软件主要具备以下功能及特征：工业过程动态可视化、数据采集和管理、过程监控与报警、生成报表、为其他企业级程序提供数据、简单控制、批次处理、SPC过程质量控制、符合IEC61131—3标准等。     

基于GQM的软件过程改进度量模型的研究

提出一种基于GQM的目标、信息及属性共同驱动的GQ(I)M的软件过程改进度量模型,并且通过统计过程控制方法 SPC来进行分析验证,能够得到软件组织过程的一组基线模型,为企业决策者在实施软件过程改进中提供有效的支持和指导,以降低缺陷率、控制风险以及提高产品质量等,满足企业希望以较低成本取得良好改进效果的需求,尤其是对中小企业更具有现实意义。     

基于Labview开发统计过程控制软件

统计过程控制是质量管理的重要内容,介绍了SPC的原理,并用Labview软件实现了SPC软件.这种开发方法应用了Labview软件提供的统计过程控制模块,方便了程序的开发,很容易被质量人员掌握,设备改动或软件应用于别的设备时,只需简单地修改参数,大大方便了统计过程控制的普及,提高企业的质量水平.     

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.     

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.     

SPC在软件过程度量中的应用及改进

在介绍软件过程度量原理的基础上,讨论Shewhart控制图的构成和分析方法。结合实例,分析统计过程控制在软件过程度量中的作用。针对传统Shewhart控制图无法区分软件过程之间影响的缺陷,借助选控图理论对现有方法在软件过程度量中的不足提出改进。有效区分软件过程的相互作用,定性和定量地分析软件过程的稳定性和性能。     

一种基于SPC的软件过程质量分析方法

一个项目的开发活动是很多软件过程的集合,不同软件过程之间关联性很强,成功地分析特定软件过程质量的关键是确保软件过程分析的独立性,剔除来自于其他过程的影响。传统Shewhart控制图基于统计假设检验理论,能够区分软件过程中的偶然因素和系统因素,但Shewhart控制图是全控图,无法区分过程之间的影响。为解决这种问题,定义软件过程的总质量和分质量,把系统因素细分为外部系统因素和内部系统因素,并总结软件过程质量诊断表,以使用控制图和选控图来帮助诊断导致软件过程质量异常的偏差源。     

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.     

Application of the generalized lambda distributions in a statistical process control methodology

In statistical process control (SPC) methodology, quantitative standard control charts are often based on the assumption that the observations are normally distributed. In practice, normality can fail and consequently the determination of assignable causes may result in error. After pointing out the limitations of hypothesis testing methodology commonly used for discriminating between Gaussian and non-Gaussian populations, a very flexible family of statistical distributions is presented in this paper and proposed to be introduced in SPC methodology: the generalized lambda distributions (GLD). It is shown that the control limits usually considered in SPC are accurately predicted when modelling usual statistical laws by means of these distributions. Besides, simulation results reveal that an acceptable accuracy is obtained even for a rather reduced number of initial observations (approximately a hundred). Finally, a specific user-friendly software have been used to process, using the SPC Western Electric rules, experimental data originating from an industrial production line. This example and the fact that it enables us to avoid choosing an a priori statistical law emphasize the relevance of using the GLD in SPC.     

软件过程度量研究与设计

首先介绍了软件能力成熟度模型CMM和过程度量框架，在CQM模型基础上提出了CQAM模型，并结合CMM模型设计了一个软件过程度量系统总体结构，在系统中采用SPC作为度量分析方法，简单介绍了其中的X-R图并给出了一个度量实例。     

Construction of quality control charts by using probability and fuzzy approaches and an application in a textile company

A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. In this study, statistical quality control and the fuzzy set theory are aimed to combine. As known, fuzzy sets and fuzzy logic are powerful mathematical tools for modeling uncertain systems in industry, nature and humanity; and facilitators for common-sense reasoning in decision making in the absence of complete and precise information. In this basis for a textile firm for monitoring the yarn quality, control charts proposed by Wang and Raz are constructed according to fuzzy theory by considering the quality in terms of grades of conformance as opposed to absolute conformance and nonconformance. And then with the same data for textile company, the control chart based on probability theory is constructed. The results of control charts based on two different approaches are compared. It’s seen that fuzzy theory performs better than probability theory in monitoring the product quality.