统计过程控制在策划和实施阶段的注意事项

SPC(统计过程控制)是企业质量管理中的重要工具之一,为质量管理提供技术支持.通过分析策划与实施统计过程控制中的关键过程,解决企业在推行SPC中遇到的问题.     

正确选择统计判别法剔除异常值

本文列举了剔除异常值常用的三个统计判别方法,包括拉依达准则、格拉布斯准则和狄克逊准则.通过比较典型的一组测量数据例子,比较了三者判别松紧的差异,讨论了在实际测量中,应怎样选择合适的统计判别法去剔除异常值.     

统计过程控制在ELISA检测过程中应用的新思路

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.实验室的检测过程实际就是生产检测结果的过程.     

统计方法在贵金属检测实验室比对中的应用

本文主要介绍实验室比对中常用统计方法及其使用前提条件、联合使用条件。并说明统计分析过程应遵循的分析步骤。     

统计过程控制在化工生产质量改进中的应用

为保证产品质量,实现质量管理的持续改进,化工企业需避免装置运行波动,需对产品生产的各个环节加以持续的监视和测量,及时发现已发生的和潜在的质量问题,并及时采取纠正和预防措施,实施质量改进。本文对统计过程控制在化工生产过程控制中所起到的基础作用及应用进行了论述。     

FMS统计过程控制技术的研究

对统计过程控制技术在柔性控制系统中的应用进行了研究，主要研究内容包括：适用于多品种、小批量生产的质量控制图；基于神经网络的质量控制图异常模式自动识别技术。     

提高实验室测量结果质量的常用方法

介绍了实验室为监视测量过程并查找、排除质量环中所有导致不合格不满意的原因,保证检测和校准服务结果的真实性、准确性、可靠性,采取的常用方法,并通过实例阐述如何绘制过程控制图。     

统计过程控制的三个基本问题

本文围绕统计过程的监测、诊断和变点时间估计三个基本问题,首先概括地介绍了过程监测的最优控制图—累积和控制图;然后,详细介绍了如何利用多重累积和控制图对过程的异变进行统计诊断;最后简述了三种常用的变点估计方法:最大似然估计,Schwarz信息判别估计和Bayesian估计。此外,文中还列出6个亟待解决的问题。     

计算结构随机振动辐射声场的统计波叠加方法

机器的机械振动大多是具有随机特性的随机过程,由其激发的声场为随机声场,对于随机声场中的信息就需要用统计的参数来描述.文中结合波叠加方法和统计方法,提出一种统计波叠加方法,利用结构表面的振速自谱函数.对随机声场中的统计声学量进行计算,不需要对每个随机样本进行单独的声学计算后再作统计分析,速度快,精度高,并对振动箱体进行了仿真分析,计算结果的最大误差为1.3%,证明了统计波叠加方法对随机声场进行计算的正确性和有效性.     

面向小批量生产的统计过程控制的研究

指出了在小批量生产环境下实施统计过程控制存在的问题,用概率积分变换理论,给出了控制过程均值、过程方差的标准化控制图,适用于小批量生产环境下对过程均值、过程方差的有效控制。     

CUSUM charts for detecting special causes in integrated process control

This paper investigates control charts for detecting special causes in an ARIMA(0, 1, 1) process that is being adjusted automatically after each observation using a minimum mean‐squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0, 1, 1) process, but it is desirable to detect and eliminate special causes that occur occasionally and produce additional process variation. It is assumed that these special causes can change the process mean, the process variance, the moving average parameter, or the effect of the adjustment mechanism. Expressions are derived for the process deviation from target for all of these process parameter changes. Numerical results are presented for sustained shifts, transient shifts, and sustained drifts in the process parameters. The objective is to find control charts or combinations of control charts that will be effective for detecting special causes that result in any of these types of parameter changes in any or all of the parameters. CUSUM charts designed for detecting specific parameter changes are considered. It is shown that combinations of CUSUM charts that include a CUSUM chart designed to detect mean shifts and a CUSUM chart of squared deviations from target give good overall performance in detecting a wide range of process changes. Copyright © 2009 John Wiley & Sons, Ltd.     

Application of X̄ control chart with modified limits in process control

The conventional Shewhart X̄ chart is developed based on the assumption that the within‐sample variation is due to the inherent process variation, and any significant variation between samples is attributed to the existence of assignable causes. In the manufacturing industry there are processes where there is variation between samples due to the inherent process variation. A straightforward application of the conventional Shewhart X̄ chart would thus result in more frequent false alarms. The problems associated with various Shewhart X̄ charts in monitoring such a process are discussed using a real data set from an integrated circuit (IC) assembly process. A Shewhart X̄ chart with modified limits is adapted for such a process. In addition to the usual ability to signal for assignable causes, the X̄ chart with modified limits is also developed as a tool to signal the need for adjustment of controllable process variables for improving the process capability. Practical application of this chart in monitoring an IC assembly process is discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

度量工业过程能力的重要指标C_(PK)

讨论了基本的过程能力指数之一CPK的重要作用及与其它指数之间的关系,以及确定的方法;最后说明CPK在使用时应该注意的问题.     

Some Modifications of the Classical Pre‐control Technique

Pre‐control is a simple technique for the initial evaluations of the capability of a process. It can be seen as a tool to get the set‐up approval or fulfilment of the specifications of a production process. As the resultant information of pre‐control should be used to adjust the process, it can be understood as a form of feedback controller. It has sometimes been considered as an alternative to statistical control charts for monitoring processes, although these tools differ in a number of ways. In this work, we propose some new alternatives to the classical pre‐control, particularly in its initial phase that aim to qualify the process, that is, to certify that it is capable. We present a comparative analysis of the power of the different alternatives. Copyright © 2003 John Wiley & Sons, Ltd.     

Integrating statistical process monitoring with feedforward control

Feedforward control is a particular form of engineering process control. If a known input variable z_t can be measured and appropriate relationships built between the input variable, a compensatory variable x_t, and the desired output y_t, then a feedforward control system can be developed. Using feedforward control, we show under both sudden jump shifts and trends in the mean of the input and compensatory variables that the use of statistical process monitoring tools, such as the exponentially‐weighted moving average and the cumulative sum (CUSUM), significantly reduces variability in both the output variable and the controller relative to the use of feedforward control alone. Copyright © 2000 John Wiley & Sons, Ltd.     

Recursive parameter estimation for categorical process control

Statistical process adjustment (SPA) is utilised prevalently in novel manufacturing scenarios. When quality characteristics rather than internal process variables are inspected for the purpose of quality control, data with different resolutions may be collected. This paper proposes a Bayesian framework for parameter estimation when only categorical observations are available. The proposed method incorporates categorical information recursively and updates parameter estimates in real time. Simulation results show that the framework is effective in utilising low-resolution information in parameter estimation, model building and process control.     

A control scheme integrating the T chart and TCUSUM chart

This article proposes an integrated scheme (T&TCUSUM chart) which combines a Shewhart T chart and a TCUSUM chart (a CUSUM‐type T chart) to monitor the time interval T between the occurrences of an event or the time between events. The performance studies show that the T&TCUSUM chart can effectively improve the overall performance over the entire T shift range. On average, it is more effective than the T chart by 26.66% and the TCUSUM chart by 14.12%. Moreover, the T&TCUSUM chart performs more consistently than other charts for the detection of either small or large T shifts, because it has the strength of both the T chart (more sensitive to large shifts) and the TCUSUM chart (more sensitive to small shifts). The implementation of the new chart is almost as easy as the operation of a TCUSUM chart. Copyright © 2010 John Wiley & Sons, Ltd.     

面向质量目标的Q控制图设计

针对大批量生产开始阶段的过程监控,提出了一种基于预定质量目标的Q控制图监控方法.其基本思路是利用面向质量目标的统计公差技术与Q控制图相结合应用,以实现大批量过程开始阶段均值和方差未知时面向质量目标的过程监控.基于质量目标建立统计公差(CP*,k*),并将该统计公差转化为基于给定置信概率的对CP和k的估计值的判定条件.通过案例分析,面向质量目标的Q控制图能够在过程保持受控状态的前提下以一定置信概率保证质量目标.     

Integrating artificial intelligence into on‐line statistical process control

Statistical process control (SPC) is one of the most effective tools of total quality management, the main function of which is to monitor and minimize process variations. Typically, SPC applications involve three major tasks in sequence: (1) monitoring the process, (2) diagnosing the deviated process and (3) taking corrective action. With the movement towards a computer integrated manufacturing environment, computer based applications need to be developed to implement the various SPC tasks automatically. However, the pertinent literature shows that nearly all the researches in this field have only focussed on the automation of monitoring the process. The remaining two tasks still need to be carried out by quality practitioners. This project aims to apply a hybrid artificial intelligence technique in building a real time SPC system, in which an artificial neural network based control chart monitoring sub‐system and an expert system based control chart alarm interpretation sub‐system are integrated for automatically implementing the SPC tasks comprehensively. This system was designed to provide the quality practitioner with three kinds of information related to the current status of the process: (1) status of the process (in‐control or out‐of‐control). If out‐of‐control, an alarm will be signaled, (2) plausible causes for the out‐of‐control situation and (3) effective actions against the out‐of‐control situation. An example is provided to demonstrate that hybrid intelligence can be usefully applied for solving the problems in a real time SPC system. Copyright © 2003 John Wiley & Sons, Ltd.     

Economic models and process quality control

Quality is an important business strategy in the economic and technological environment of today. To achieve high product quality, it is important to take explicit account of the cost of quality, and to use this cost as another management control. A new direction for achieving a cost-effective quality management system is to design statistical process controls so as to directly incorporate quality costs. This paper discusses the major approaches to the economic design of statistical process controls, and compares several different model formulations. The practical implication of these techniques is stressed. In particular, two economic models of the chart are presented: a full economic model requiring a user-specified process and nine cost parameters, and a semi-economic model using five user-specified parameters. Both of these could serve as approaches to reducing the total cost of process control. Because of its simplicity in application the semi-economic model should gain greater acceptance by practitioners for the design of process control techniques.