一种变参数的控制图方法及其稳健分析

针对在实际应用中,现有的控制图仅仅对某个固定大小的偏移有效的问题,首先提出了一种变参数的控制图设计方法,并对这种控制图的平均运行长度的计算方法进行了探讨.然后讨论了变参数控制图的稳健设计方案,通过与常规控制图和EWMA图进行比较,说明提出的监控方法对过程中出现的不同程度的偏移都是有效的.     

重复性与再现性对控制图的影响分析

测量系统的波动是测量值变异的一个重要来源,它对于过程控制和控制图的灵敏性都会造成影响。基于测量系统的重复性和再现性分析,讨论测量系统能力大小对X控制图灵敏性的影响。结果表明,符合能力评价指标要求的测量系统对控制图灵敏性的影响不是很明显,否则影响较大,并通过一个例子验证此结果。     

Generally weighted moving average control chart for monitoring process variability

Most applications of the EWMA control chart for monitoring processes depend on detecting shifts in the process mean. The problem of detecting an increase in process variability, which can also strongly affect the quality of products, is perhaps more important. When a process moves from the pilot phase to the production phase, the mean may not shift but the variation will probably increase because new sources of variation are introduced, including new people and materials. A simulation is performed to evaluate the ARL to false alarm and to monitor the change in the process variability of the EWMA control chart and the GWMA control chart. An extensive comparison reveals that the GWMA control chart is more sensitive than the EWMA control chart in monitoring the variance of a process. The results of this study can be applied to monitor the process variability in automated industries.     

多元预控图监控准则优化研究

多元预控图将一元预控图的监控变量由一元扩展到多元,是分析和监控两个及两个以上相关质量特性的有效方法,是复杂生产过程中控制产品质量的重要工具,但也存在精度偏低和监控效率低下等问题。为此,在一元监控图基础上,为降低多元预控图的误判概率,按增加单个样本所能提高的概率来调整抽样数,由5个增加到10个,在新的抽样数条件下调整监控准则,由4个增加到6个,并采用平均链长度(ARL)为基准,对新旧监控准则的监控效果进行比较并获取最优监控准则,提升了多元控制图的的监控精度和效率。     

A hybrid control scheme to enhance the detection ability of process mean shifts

The hybrid use of Robert’s EWMA and Crosier’s cumulative sum (CUSUM) control schemes is shown to result in a control scheme with high-performance detection of any magnitude of process mean shifts. The parameters of the hybrid scheme are found by adapting the average run length (ARL) to those of a set of locally optimal EWMA schemes in the best way. In this report, an enhancement to the hybrid scheme is considered. First, the ARLs of the hybrid scheme are derived by using the extrapolation method proposed by Waldmann. Second, an optimization of parameter settings is stated and solved. An extensive comparison shows that the ARL properties of the hybrid scheme are comparable to those of the combined Shewhart-CUSUM scheme. Finally, numerical results and examples are presented to demonstrate the efficiency of the hybrid scheme.     

Sequential monitoring of manufacturing processes: an application of grey forecasting models

This study used statistical control charts as an efficient tool for improving and monitoring the quality of manufacturing processes. Under the normality assumption, when a process variable is within control limits, the process is treated as being in-control. Sometimes, the process acts as an in-control process for short periods; however, once the data show that the production process is out-of-control, a lot of defective products will have already been produced, especially when the process exhibits an apparent normal trend behavior or if the change is only slight. In this paper, we explore the application of grey forecasting models for predicting and monitoring production processes. The performance of control charts based on grey predictors for detecting process changes is investigated. The average run length (ARL) is used to measure the effectiveness when a mean shift exists. When a mean shift occurs, the grey predictors are found to be superior to the sample mean, especially if the number of subgroups used to compute the grey predictors is small. The grey predictor is also found to be very sensitive to the number of subgroups. This revised version was published online in May 2005 with a correction to the author’s affiliation.     

An evolutionary economic-statistical design for VSI X control charts under non-normality

Since Reynolds et al. [1] proposed the variable sampling interval (VSI) control chart, the statistical properties of the chart have been discussed and shown to be better than the traditional control chart in which the sampling intervals length are fixed. However, the VSI charts are, like the traditional charts, still costly when they are used for the prevention of defective products. For this reason, an appropriate design is necessary before it is used. In this paper, a VSI control chart is used for monitoring non-normal process data, which is widely encountered in practice. Then, a cost-quality model based on the Burr distribution is proposed for constructing an economic-statistical design for the VSI chart. The design of the chart has been developed considering the optimisation of the cost function in this model posed by statistical constraints. The design parameters can be derived through the evolutionary search method, and the overall finding indicates that the designed VSI chart always outperforms the traditional control chart with respect to the expected cost per unit time. This model is also suitable for normally distributed data; thus, it is suitable for a general application.     

用VC实现质量控制图的绘制与分析

介绍了利用Microsoft Visual C 6.0开发工具,在Windows平台上进行质量控制图的绘制和分析的方法。通过质量控制图,可以作出正确的判断,有效地监控生产过程中的质量变化情况。     

监控均值标准差的可变抽样区间EWMA图经济设计

为了在提高过程监控效率的同时降低过程监控费用,针对同时监控过程均值和标准差变化的情形,研究可变抽样区间的指数加权移动平均控制图的经济设计问题.首先建立可变抽样区间,同时监控均值标准差指数加权移动平均控制图;其次对其进行经济设计,使单位时间期望费用函数最小,以确定控制图参数的最优值;然后利用遗传算法来寻找经济模型的参数最优解;最后对可变抽样区间同时监控均值标准差指数加权移动平均控制图的经济模型进行灵敏度分析和最优性分析.通过最优性分析得出结论:基于经济模型设计的可变抽样区间指数加权移动平均控制图比传统的控制图具有较小的费用.     

A new adaptive control chart for monitoring process mean and variability

Traditionally, an $\bar{X}$ chart is used to control the process mean, and an R chart is used to control the variance. However, these charts are not sensitive to the small shifts in the processes. The adaptive charts might be considered if the aim is to detect process changes quickly. In this paper, we propose a new adaptive single control chart which integrates the exponentially weighted moving average procedure with the generalized likelihood ratio test statistics for jointly monitoring both the process mean and variability. This new chart is effective in detecting the disturbances that shift the process mean, increase or decrease the process variance, or lead to a combination of both effects.     

An economic design for variable sampling interval MA control charts

Most of the studies done on the economic design of control charts focus on a fixed-sampling interval (FSI); however, it has been discovered that variable-sampling-interval (VSI) control charts are substantially quicker in detecting shifts in the process than FSI control charts due to a higher frequency in the sampling rate when a sample statistic shows some indication of a process change. In this paper, an economic design for a VSI moving average (MA) control chart is proposed. The results of a numerical example adopted from an actual case indicate that the loss cost of VSI MA control charts is consistently lower than that of the FSI scheme.Design variables n Sampling size for each moving plot - h_a Subsequent sampling interval when preceding sample mean is located at sub-control region I_a, a=1,2,..., - Number of different sampling-interval lengths, 2 - k_a Threshold limit expressed in units of - k₁ Control limit expressed in units of Parameters related to assignable cause µ₀ Target mean - True-process standard deviation - Magnitude of an assignable cause expressed in units of - Occurrence rate of an assignable cause per unit timeCost and technical parameters D Average time taken to find and repair an assignable cause after detection - e Time for a sample to be taken, transmitted to laboratory, and results phoned back to process control room - M Income reduction when =₀+ - T Average cost of looking for an assignable cause when a false alarm occurs - W Average cost of looking for and repairing an assignable cause when one does exist - F_c Fixed cost per subgroup of sampling, inspecting, evaluating and plotting - V_c Variable cost per subgroup of sampling, inspecting, evaluating and plotting     

Configurable monitoring system for industrial process control

A data-centered architecture for an industrial process monitoring system (PMS) is proposed. In such a system, the elements of graphical user interface (GUI) can be used to simulate the working process of practical devices by their state data, because a change of devices’ state will cause some properties of graphic elements to vary. And by operating the graphic elements, the user can first handle the monitoring data, and then operate the devices indirectly since the changed monitoring data will be inputted into the devices, and ensure their control. A configurable platform, which consists of tools such as project manager, GUI developer, system builder, etc., is developed to customize a specific PMS for different types of devices. The procedure of configuring a specific PMS with the platform is detailed. Then, the reconstructing and working process of the specific PMS is described.     

液位对象的自校正动态矩阵控制

本文针对液位对象参数时变和纯时延的特点,用自校正和动态矩阵控制的思路设计进行系统设计,介绍了系统设计方法和检验结果。     

Application of the PSO–SVM model for recognition of control chart patterns

Control chart patterns are important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Accurate recognition of control chart patterns is essential for efficient system monitoring to maintain high-quality products. This paper introduces a novel hybrid intelligent system that includes three main modules: a feature extraction module, a classifier module, and an optimization module. In the feature extraction module, a proper set combining the shape features and statistical features is proposed as the efficient characteristic of the patterns. In the classifier module, a multi-class support vector machine (SVM)-based classifier is proposed. For the optimization module, a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. In this module, it the SVM classifier design is optimized by searching for the best value of the parameters that tune its discriminant function (kernel parameter selection) and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed algorithm has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using particle swarm optimizer.     

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.     

A CUSUM scheme with variable sample sizes for monitoring process shifts

The cumulative sum scheme (CUSUM) and the adaptive control chart are two approaches to improve chart performance in detecting process shifts. A weighted loss function CUSUM scheme (WLC) is able to monitor both the mean shift and the increasing variance shift by manipulating a single chart. This paper investigates the WLC scheme with a variable sample sizes (VSS) feature. A design procedure is firstly proposed for the VSS WLC scheme. Then, the performance of the chart is compared with that of four other competitive control charts. The results show that the VSS WLC scheme is more powerful than the other charts from an overall viewpoint. More importantly, the VSS WLC scheme is simpler to design and operate. A case study in the manufacturing industry is used to illustrate the chart application. The proposed VSS WLC scheme suits the scenario where the strategy of varying sample sizes is feasible and preferable to pursue a high capability of detecting process variations.     

Determining dimensions for process planning: A backward derivation approach

Computer-aided process planning has shown its great importance in computer integrated manufacturing systems. Dimensioning and tolerancing plays a key role in process planning since the final workpiece must ensure conformance with the dimensions in its blueprint. This paper proposes a simple, efficient approach to derive working dimensions for process planning, based on the tolerance chart technique. The paper first reviews briefly some approaches previously presented, such as the mathematical linear equation approach and the graph tracing approach, it then proposes the backward derivation approach. By setting up the origin of a coordinate system at the left-most surface in a blueprint (B/P), the approach first determines the distances from all the B/P surfaces to the origin by use of the B/P dimensions. Then by updating these dimensions, the final working dimensions are calculated backwards by a simple arithmetical equation.     

C pm MPPAC for manufacturing quality control applied to the precision voltage reference process

The multiprocess performance analysis chart (MPPAC) based on the process capability index C_pm, called C_pm MPPAC, is developed for analysing the manufacturing quality of a group of processes in a multiple process environment. The C_pm MPPAC conveys critical information of multiple processes regarding the departure of the process and process variability from one single chart. Existing research works on MPPAC are restricted to obtaining quality information from one single sample of each process ignoring sampling errors. The information provided from existing MPPAC charts, therefore, is unreliable and misleading and results in incorrect decisions. In this paper, we consider the natural estimator of C_pm based on multiple samples. Based on the natural estimator of C_pm, we consider the sampling errors by providing an explicit formula with the Matlab program to obtain the estimation accuracy of the C_pm. We tabulate the sampling accuracy of C_pm for sample size determination so that the engineers/practitioners can use it for their in-plant applications. An example of multiple precision voltage reference (PVR) processes is presented to illustrate the applicability of C_pm MPPAC for manufacturing quality control.     

A KPI-based process monitoring and fault detection framework for large-scale processes

Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods.     

聚酯树脂生产过程控制系统

介绍了一种用于不饱和聚酯树脂生产的过程控制系统及其开发方案。用该系统替代相应的进口设备,取得了很好的效果;几年来,为企业带来了较大的经济效益．