基于SPSS的可重构统计过程控制系统

为满足制造企业多样、敏捷的统计过程控制（SPC)需求,实现单元应用以及与其他制造信息系统的集成应用,通过分析和构建SPC领域模型,提出并实现了基于社会科学统计软件包(SPSS)的可重构统计过程控制系统。通过引入SPSS组件,执行SPSS Syntax命令,基于SPSS强大的统计分析功能实现对SPC的可视化统计分析,并以具有扩展点的组件化可重构架构满足企业应用模式和配置模式的可重构需求。最后以实例说明系统具有良好的可用性,为制造企业全面质量控制提供了一种数字化手段和应用平台。     

小批量多元控制图建模方法研究

在分析研究现有小批量及多元控制图相关理论的基础上,基于Kalman滤波原理,提出一种综合解决小批量多元过程控制的建模方法。仿真实验及应用实例表明,该建模方法能够充分利用已经取得的数据,动态建立控制模型,从而解决小批量生产过程中建模数据不足的问题。     

基于统计过程控制的协同推荐攻击检测方法

针对恶意攻击者利用协同推荐系统用户偏好敏感的缺陷向系统中注入虚假数据破坏推荐结果真实性的问题,提出基于统计过程控制(SPC)的协同推荐攻击检测方法。该方法将用户概貌项目评价数偏离度作为服务质量控制属性构建休哈特控制图,利用判异规则检测攻击用户,从而完善协同推荐系统模型。实验证明这种检测方法对各种不同的攻击模型都有较高的检测准确率和查全率。     

一种SCADA模式SPC系统的设计与实现

结合5C技术,介绍一种基于监视控制和数据采集(SCADA)的统计过程控制(SPC)系统设计架构,包括:现场多工序多点测量采集系统设计;通过对异常判定诊断模型分析,介绍软件如何实现测量数据实时跟踪回溯及实时选控图异常判定告警等.     

基于实时数据处理的钢铁加热炉在线质量监控方法

针对钢铁企业加热炉生产过程中数据流不易于集中管理和分析,无法有效地进行生产监控及在线质量分析问题,提出了一种基于实时数据分析的生产监控与在线质量分析的方法。结合实时数据库和关系数据库,引入六西格玛管理手段和控制图技术,设计和实现了加热炉生产监控与在线质量分析系统。系统具有实时数据处理、生产监控与质量分析、产品在线质量监控等功能。应用结果表明,系统有效地集成了企业的生产数据和标准数据,实现了对加热炉生产的实时监控与质量判定。     

Utilization of statistical process control (SPC) in emergent software organizations: Pitfalls and suggestions

Common wisdom in the domain of software engineering tells us that companies should be mature enough to apply Statistical Process Control (SPC) techniques. Since reaching high maturity levels (in CMM or similar models such as ISO 15504) usually takes 5–10 years, should software companies wait years to utilize Statistical Process Control techniques? To answer this question, we performed a case study of the application of SPC techniques using existing measurement data in an emergent software organization. Specifically, defect density, rework percentage and inspection performance metrics are analyzed. This paper provides a practical insight on the usability of SPC for the selected metrics in the specific processes and describes our observations on the difficulties and the benefits of applying SPC to an emergent software organization.     

An integrated model based on statistical process control and maintenance

The close relationship between quality and maintenance of manufacturing systems has contributed to the development of integrated models, which use the concept of statistical process control (SPC) and maintenance. Such models not only help to improve quality of products but also lead to lower maintenance cost. In this paper, an integrated model is presented which considers complete failure and planned maintenance simultaneously. This model leads to six different scenarios. A new procedure for calculating average cost per time unit is also presented. Finally, a numerical example is used to evaluate sensitivity of the model parameters and compare performance of the developed model to a planned maintenance model. Results indicate satisfactory performance for the developed model.     

基于AHP算法和SPC的软件过程度量方法的研究

目前,软件过程度量理论和方法还不够完善,而高效准确的过程定量管理是成功进行软件过程改进的保障,为此提出了一种结合层次分析（AHP）算法和统计过程控制（SPC）的软件过程度量新方法。方法遵循集成能力成熟度模型（CMMI）构架,从软件过程角度对软件项目进行度量。最后以实例证明本方法能有效支持软件过程的度量与改进。     

A component-based simulation environment for statistical process control systems analysis

This paper describes a simulation environment, called Prosim, which permits a user to define components, subsystems, and their interconnections to analyse a statistical process control (SPC) system. The components and systems are defined and analysed interactively. A library of standard SPC objects containing models for the Xbar, range, exponential weighted moving average, p-chart and other SPC techniques have been created which help define the control application. The PC-based tool is tested on theoretical, and real data, and is useful for the design and trouble shooting of a manufacturing system. It is also an effective teaching and research tool.     

Variable window adaptive Kernel Principal Component Analysis for nonlinear nonstationary process monitoring

On-line control of nonlinear nonstationary processes using multivariate statistical methods has recently prompt a lot of interest due to its industrial practical importance. Indeed basic process control methods do not allow monitoring of such processes. For this purpose this study proposes a variable window real-time monitoring system based on a fast block adaptive Kernel Principal Component Analysis scheme. While previous adaptive KPCA models allow only handling of one observation at a time, in this study we propose a way to fast update or downdate the KPCA model when a block of data is provided and not only one observation. Using a variable window size procedure to determine the model size and adaptive chart parameters, this model is applied to monitor two simulated benchmark processes. A comparison of performances of the adopted control strategy with various Principal Component Analysis (PCA) control models shows that the derived strategy is robust and yields better detection abilities of disturbances.     

基于大数据统计分析的变电站巡视质量控制策略

变电站内的各类巡视记录数据具备大数据的5V特性,数量大、高速传输、类型多样、合理范围内的数据低价值密度、具备时刻相关的设备真实反应等特点。而且,对变电站管理而言,巡视、排查工作的重要性不言而喻。全面巡视记录大量数据,这些数据包括外观、位置指示等大量无法精确量化的巡视数据,也包括刻度、计数、表计、遥测等能反映设备实时状态的精确的数据,应用这些数据必须先设置统一的分类分析原则,便于自动进行数据分析,同时使分析结果更具有倾向性,准确表达设备状态。本文通过合理利用变电站巡视大数据,以加强巡视过程中的质量控制为目的,提出了“三最法”巡视数据分析策略。并以此为基础,分析大数据统计结果,预判设备状态,建立了体验化巡视复询制度。该方法的应用为变电站的可靠、安全、稳定经济运行提供了有力的保障。     

基于工厂信息的实时数据流分析与全过程质量监控

针对某钢铁企业生产过程中的生产信息不畅通、产品质量无法追踪问题,开展了基于工厂信息（PI）的实时数据流分析与全过程质量监控方法的研究。着重研究了实时数据流分割和过程监控,提出基于统计质量控制（SQC）图和工序性能指标的统计监控方法,并开发了一个产品技术质量监控系统,应用结果表明基于PI的实时数据流分析与产品质量监控实现了企业对生产工序质量的监控,以及关键生产工艺的识别与改进。     

基于PCED的过程控制系统安全分析方法

针对常用安全分析技术应用中存在的不足,提出一种基于过程控制事件框图（PCED）的计算机过程控制系统安全分析方法。该方法根据系统的特点建立描述其控制逻辑的PCED模型,构造＂动作-部件-变量＂的影响路径,通过算法分析控制逻辑的安全性。以计算机控制化学反应过程系统为例用该方法进行安全分析。     

Airborne lidar bathymetry: assessing quality assurance and quality control methods with Leica Chiroptera examples

Airborne lidar bathymetry (ALB) is an effective advanced technology for mapping and measuring water depth in shallow water coastal zones as well as inland freshwater bodies such as rivers and lakes. The ability of light beams to detect and traverse shallow water columns has provided valuable information about unmapped and often poorly understood coastal and inland waterbodies. ALB surveys require specific best practices and quality management procedures to provide the highest-quality end product. Implementing quality assurance procedures before the survey commences and frequent quality control checks during and after the survey are essential steps. In this article, we summarize overall ALB development history, discuss specific ALB requirements, and provide examples that reflect our experiences of the Leica Chiroptera ALB system. Supplemental surveys, in situ measurements, and developing in-house algorithms are all beneficial and have the means to increase confidence and versatility of lidar bathymetry.     

A multivariate statistical controller for on-line quality improvement

Producing good quality products is an important process control objective. However, achieving this objective can be very difficult in a continuous process, especially when quality measurements are not available on-line or they have long time delays. In this paper, a control approach using multivariate statistical models is presented to achieve this objective. The goal of the control approach is to decrease variations in product quality without real time quality measurements. A PCA model which incorporates time lagged variables is used, and the control objective is expressed in the score space of this PCA model. A controller is designed in the model predictive control (MPC) framework, and it is used to control the equivalent score space representation of the process. The score predictive model for the MPC algorithm is built using partial least squares (PLS). The proposed controller can be developed from and implemented on top of existing PID control systems, and it is demonstrated in two case studies, which involve a binary distillation column and the Tennessee Eastman process.     

基于系统理论过程分析的安全关键软件安全性验证方法

现代安全关键系统的功能实现越来越依赖于软件,这导致软件的安全性对系统安全至关重要,而软件的复杂性使得采用传统安全性分析方法很难捕获组件交互过程带来的危险。为保证安全关键系统的安全性,提出一种基于系统理论过程分析（STPA）的软件安全性验证方法。在安全控制结构基础上,通过构建带有软件过程模型变量的过程模型,细化分析危险行为发生的系统上下文信息,并以此生成软件安全性需求。然后通过设计起落架控制系统软件,采用模型检验技术对软件进行安全性验证。结果表明,所提方法能够在系统级层面有效识别出软件中潜在的危险控制路径,并可以减少对人工分析的依赖。     

Systematic design of an optimal control system for the SHARON-Anammox process

A systematic design of an optimal control structure for the SHARON-Anammox nitrogen removal process is studied. The methodology incorporates two novel features to assess the controllability of the design variables candidate for the regulatory control layer: (i) H_∞ control method, which formulates the control problem as a mathematical optimization problem, and (ii) close-loop disturbance gain (CLDG) plots. It is shown that the methodology is especially appropriate for bioreactors. The solution of the mixed sensitivity stacked H_∞ control problem ranked the combinations of controlled variables (CVs). The best candidates to CVs were paired with the manipulated variables using the relative gain array. The proposed control structure was further analyzed and verified for disturbance rejection using the CLDG plots. The optimal pairing of CVs with the actuators (k_La and acid/base addition) is found to be dissolved oxygen (DO) and pH in the SHARON reactor. Furthermore, to relate the controller actions to process operation objective, nitrogen removal efficiency, two cascade control systems are designed. The first cascade loop controls TNN/TAN ratio in the influent to the Anammox reactor by adjusting the set point for DO in the regulatory layer, while the second cascade loop controls the nitrogen removal efficiency (i.e. effluent TNN and TAN) by adjusting the TNN/TAN ratio at the effluent of the SHARON reactor. The control system is evaluated and benchmarked using a set of realistic dynamic scenario simulations, demonstrating that the different control strategies successfully maintain stable and high nitrogen removal efficiency. The nested cascade control structure shows the best performance, removing up to 95% of the influent ammonia. Both the control design methodology and the resulting optimal control structures are expected to contribute to stable operation and control of these emerging nitrogen removal technologies.     

一种Windows Services进程控制方法的探讨与实现

Win32平台下的Windows Services是一种强大的后台程序管理模式。如果将服务程序要执行的内容与服务程序剥离写成若干单独的可执行程序,而只在服务程序里面生成它们的进程,则不但可以实现后台程序的开机启动,还可以更加有效地管理它们。     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Historical data analysis based on plots of independent and parallel coordinates and statistical control limits

Interactive multidimensional visualisation based on parallel coordinates has been studied previously as a tool for process historical data analysis. Here attention is given to improvement of the technique by the introduction of dimension reduction and upper and lower limits for separating abnormal data to the plots of coordinates. Dimension reduction using independent component analysis transforms the original variables to a smaller number of latent variables which are statistically independent to each other. This enables the visualisation technique to handle a large number of variables more effectively, particularly when the original variables have recycling and interacting correlations and dependencies. Statistical independence between the parallel coordinates also makes it possible to calculate upper and lower limits (UL and LL) for each coordinate separating abnormal data from normal. Calculation of the UL and LL limits requires each coordinate to satisfy Gaussian distribution. In this work a method called the Box–Cox transformation is proposed to transform the non-Gaussian coordinate to a Gaussian distribution before the UL and LL limits are calculated.