Integrating multivariate engineering process control and multivariate statistical process control

Multivariate engineering process control (MEPC) and multivariate statistical process control (MSPC) are two strategies for quality improvement that have developed independently. MEPC aims to minimize variability by adjusting process variables to keep the process output on target. On the other hand, MSPC aims to reduce variability by monitoring and eliminating assignable causes of variation. In this paper, the use of MEPC alone is compared to using the MEPC coupled with MSPC. We use simulations to evaluate the average run lengths (ARL) and the averages of the performance measure. The simulation results show that the use of both MEPC and MSPC can always outperform the use of either alone. To detect small sustained shifts of the mean vector, combing MEPC with a multivariate generally weighted moving average (MGWMA) chart (MEPC/MGWMA) is more sensitive than the MEPC/multivariate exponentially weighted moving average (MEWMA) chart and MEPC/Hotelling’s χ² chart. An example of the application, based on the proposed method, is also given.     

An integrated framework of statistical process control and design of experiments for optimizing wire electrochemical turning process

Design of experiments (DOE) and statistical process control (SPC) have been separately used in many traditional and non-traditional machining processes, but recently these two approaches are being combined and reevaluated for more effective use and accurate conclusions. DOE and SPC are very efficient tools to maintain the process on target and within boundaries of natural variations and to achieve the maximum accuracy and effectiveness of an experimental program. This paper proposes an integrated framework of SPC and DOE to execute the experimental procedures and to investigate a reliable mathematical model for optimizing the wire electrochemical turning process (WECT). WECT is a non-traditional machining process which has tremendous applications in modern industries especially in the aerospace and military industries. Response surface methodology is used to determine the sufficient number of experiments and also the recommended values of the input parameters. Univariate and multivariate control charts are used to assess the statistical control of the output parameters. Multi-objective optimization is conducted for determining the optimum values of the input parameters.     

An intelligent system for multivariate statistical process monitoring and diagnosis

A knowledge-based system (KBS) was designed for automated system identification, process monitoring, and diagnosis of sensor faults. The real-time KBS consists of a supervisory system using G2 KBS development software linked with external statistical modules for system identification and sensor fault diagnosis. The various statistical techniques were prototyped in MATLAB, converted to ANSI C code, and linked with the G2 Standard Interface. The KBS automatically performs all operations of data collection, identification, monitoring, and sensor fault diagnosis with little or no input from the user. Navigation throughout the KBS is via menu buttons on each user-accessible screen. Selected process variables are displayed on charts showing the history of the variables over a period of time. Multivariate statistical tests and contribution plots are also shown graphically. The KBS was evaluated using simulation studies with a polymerization reactor through a nonlinear dynamic model. Both normal operation conditions as well as conditions of process disturbances were observed to evaluate the KBS performance. Specific user-defined disturbances were added to the simulation, and the KBS correctly diagnosed both process and sensor faults when present.     

智能统计工序质量控制的体系研究

针对统计工序质量控制的要求,提出了智能控制体系的基本框架,论述了控制图模式的分类及其表达。对智能统计工序质量控制的控制图模式识别、控制图异常模式的参数估计和诊断分析专家系统3个主要方面进行了分析,并提出了解决方案和系统模型。在模型构造中,采用小波概率神经网络进行控制图的模式识别和控制图异常模式的参数估计。模拟仿真和实际应用结果表明：该方法结构简单、收敛速度快、识别准确率高,能够满足控制图在线检测和分析的需要。     

Decision-making in detecting and diagnosing faults of multivariate statistical quality control systems

A new methodology is proposed in this paper to both monitor an overall mean shift and classify the states of a multivariate quality control system. Based on the Bayesian rule (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005), the belief that each quality characteristic is in an out-of-control state is first updated in an iterative approach and the proof of its convergence is given. Next, the decision-making process of the detection and classification the process mean shift is modeled. Numerical examples by simulation are provided in order to understand the proposed methodology and to evaluate its performance. Moreover, the in-control and out-of-control average run length (Montgomery, Introduction to statistical quality control, 5th edn. Wiley, New York, USA, 2005) of the proposed method are compared with the ones from the well-known Multivariate Cumulative Sum (MCUSUM), Multivariate Exponentially Weighted Moving Average (MEWMA) and Hotelling T ² methods in different scenarios of mean shifts. The results of the simulation study show that the proposed methodology performs better than other methods for all shifts of the process mean. Additionally, the estimated probabilities of making correct classifications by the proposed approach are encouraging.     

Pattern recognition for statistical process control charts

Control charts are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Patterns displayed on control charts can provide information about the process. This paper describes the development of a pattern recognition system designed to detect and analyse various patterns that can occur on statistical quality control charts. The system looks not only for simple patterns, such as trend, shift and stratification, but also for superimposed patterns, such as trend + shift. The effect of noise associated with individual patterns is also analysed. The benefits of the approach compared with the alternatives are discussed.Notation N _i ith value of the noise series - N _T noise tolerance - x _i ith data item from a number sequence - r _i seed for random number simulation - adjacent difference - standard deviation - mean of the data - A slope of a straight line - B constant - C constant - i indexing integer - j indexing integer - k total number of samples - l starting point of a pattern on control chart - m ending point of a pattern on control chart - n size of samples - ptn pointer to the pattern identified - slope slope for trend patterns - X normally distributed variate arising from simulation - CL centre-line - LCL lower control limit - LOSL lower one-sigma limit - LWL lower warning limit - UCL upper control limit - UOSL upper one-sigma limit - UWL upper warning limit     

智能重构制造控制系统集成框架

为提高制造控制系统的智能重构能力,扩展了IEC 61499基本功能块模型,提出了知识功能块新概念,并将其作为功能单元构建了智能重构制造控制系统集成框架.在阐述了智能重构与智能控制并行执行过程的基础上,提出了基于Web Services面向服务的制造控制系统软件体系结构,并采用统一化建模语言建模语言进行了系统设计.通过一个原型系统运行实例,验证了IEC 61499功能块标准、Web服务中间件和面向服务哲理在实现智能重构制造控制系统方面的有效性.     

Experimental validation of a new statistical process control feature for damage detection

A novel statistical process control-based method for damage detection in cantilever-like structures is presented in this paper . It is based on time-domain data obtained from the free vibrations of the first mode of the structure, measured at only one point. The concept of signal length is introduced here as a feature for statistical process control. A statistical model consistent with extreme value theory is fitted to the operational data and used to establish control limits. A strategy based on controlling runs is adopted to increase the sensitivity of the detection. The procedure was applied to a four-storey steel frame, which was subjected in turn to mass and stiffness modifications by adding masses or loosening bolts in a beam-column connection. The results were successful; the proposed procedure was able to detect an increase of 0.04% of the total mass of the frame and loosening of one out of 152 bolts.     

Integration of advanced process control and full-scale dynamic simulation

In the process control industry, multivariable model predictive controller and dynamic simulation for operator training are usually available in separate packages. It is very difficult for the operators and plant engineers to find good tools for them to get trained in multivariable advanced process control. This paper presents a system, which integrates the advanced process control and full-scale dynamic simulation. The advanced process control uses multivariable model predictive control techniques. The model used in the predictive control algorithms is generated from the dynamic simulated process. The advanced process controller can control the simulated plant directly, or through a DCS system to control the simulated plant. The combined system provides an excellent environment for training operators in process operation with multivariable advanced process control. The same environment is also very useful for engineers in designing and tuning the advanced process controllers, and in testing communication between the advanced process controller and the DCS systems, or the other type of process control systems.     

基于过程方法的统计过程控制技术应用研究

统计过程控制(SPC)就是运用质量特性,使用控制图等统计技术来分析过程或其输出,确保生产过程始终处于统计控制状态,以保证产品质量,提高经济效益的技术,是解决和控制产品质量的一个理想手段.SPC技术在欧美发达国家得到普遍的应用,但在我国由于缺乏结合本国制造业管理基础等方面的应用研究,能成功利用SPC进行产品品质监控和改善的企业很少.本文根据质量管理现场审核过程中发现的问题及薄弱环节,借鉴美国福特和通用汽车等公司使用方法,结合江苏等地企业的管理基础,提出了基于过程方法的SPC统计过程控制系统理论并对该理论进行了应用研究,在此基础上结合实际应用案例提供了一种正确运用SPC方法.本文的应用研究对发挥SPC在质量管理中的作用,推广SPC应用具有重要的现实意义.     

Improving bonding strength of tape-automated bonding technology by implementing statistical process control: a case study

The purpose of this paper is to improve the bonding strength of tape-automated bonding (TAB) technology in supertwisted nematic liquid crystal display (STN LCD) module manufacturing by double-process control. First, the quality characteristic – the output of the process – is monitored by control charts and the variation of the process is reduced. Later, it is found that the quality characteristic is affected by critical input process parameters. These critical input process parameters and the quality characteristic are therefore monitored simultaneously. By this double-process control, the deviation of the process is further reduced. The results show that process capability and yield rate are increased.     

Noise tolerance for pattern recognition for statistical process control

This paper describes an investigation into the performance of an algorithmic pattern recognition system for statistical process control applications. The study, based on the application of simulation techniques, investigates whether the system can recognise patterns in the presence of noise, and how much noise the Pattern Recogniser can accept in relation to individual patterns. The level of noise tolerance for each pattern is analysed using simulated data. The simulation procedures are presented and the results are discussed. The pattern recognition system was found to be tolerant of noise, the level of tolerance generally depending on the pattern specification.Notation N _i ith data item from a noise series - x _i ith data item from a number sequence - N _T noise tolerance - _n standard deviation for noise - _s standard deviation for signal - standard deviation - adjacent difference - CL centre-line - i indexing integer - j indexing integer - k total number of samples - l starting point of a pattern on control chart - LCL lower control limit - LNTL lower noise tolerance limit - LOSL lower one sigma limit - LWL lower warning limit - N/S noise to signal ratio - slope slope for trend patterns - UCL upper control limit - UNTL upper noise tolerance limit - UOSL upper one sigma limit - UWL upper warning limit     

路面机械设备改进与质量过程控制

在阐述路面施工质量过程框架的基础上,提出了对机械设备的改进方法,并使用组态技术解决了对机械设备运行过程的实时监控问题.     

Automatic statistical process control of a CNC turning centre using tool offsets and tool change

This work deals with the implementation of automatic statistical process control (ASPC) on a CNC turning centre using automatic in-cycle gauging with tool offsets and tool change. Owing to the increasing need for a fast and reliable quality control system and in order to cope with the high output rates achievable in manufacturing industry today, a closed-loop ASPC system has been proposed. The technique of automatic in-cycle gauging is introduced in preparation for a detailed examination of the ASPC system which has been procured for this work.     

A heuristic threshold policy for fault detection and diagnosis in multivariate statistical quality control environments

In this paper, a heuristic threshold policy is developed to detect and classify the states of a multivariate quality control system. In this approach, a probability measure called belief is first assigned to the quality characteristics and then the posterior belief of out-of-control characteristics is updated by taking new observations and using a Bayesian rule. If the posterior belief is more than a decision threshold, called minimum acceptable belief determined using a heuristic threshold policy, then the corresponding quality characteristic is classified out-of-control. Besides using a different approach, the main difference between the current research and previous works is that the current work develops a novel heuristic threshold policy, in which in order to save sampling cost and time or when these factors are constrained, the number of the data gathering stages is assumed limited. A numerical example along with some simulation experiments is given at the end to demonstrate the application of the proposed methodology and to evaluate its performances in different scenarios of mean shifts.     

Fuzzy ART神经网络在统计质量控制中的应用

FuzzyART是在ART神经网络的基础上发展出的一种无导师学习型神经网络技术,将之引入到工序质量控制过程中,通过应用FuzzyART网络对生产过程质量数据直方图进行聚类分析,实现对生产过程异常状态的自动判别,得到了令人满意的结果。尤其在捕捉突发异常方面较之有监督学习神经网络有着很大的优势。     

SPC技术在小批量多品种短期生产中的应用

本文分析了传统SPC技术在小批量多品种短期生产中质量控制所存在的问题,应用数理统计方法．得出一种基于正态过程．采取以数据标准化为基础的Z-MR图．可有效用于小批量多品种下对短期生产实施过程质量控制。     

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。     

基于统计过程控制与维护策略的联合经济设计模型

在已有研究的基础上,对传统的统计过程控制模型进行拓展,融合了设备维护的思想,给出了联合经济模型。该模型考虑了生产过程出现的三种情况,运用控制图监视每种情况的生产状态,通过田口质量损失函数定义相应状态的质量损失成本,并根据过程所处的生产状态对其实施相应的维护策略。过程产生的质量成本与维护成本计入总生产成本,以获得最优的控制图界限系数、抽样检验间隔、实施计划维护前需要的样本数量及样本大小,使得生产过程在生产周期内的总期望生产成本最小。模型结果有助于生产人员根据特定的生产过程情况做出科学经济的生产计划和决策。采用单纯型搜索法对模型进行求解,求解过程通过MATLAB工具箱实现,通过数值例子和参数敏感度分析说明了该模型的有效性。