Distribution-free monitoring of univariate processes

We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts for applications when the process data are not normal, and compare them with the traditional CUSUM chart and two recent distribution-free control charts. Some empirical guidelines are provided for practitioners to choose a proper control chart for a specific application with non-normal data.     

复杂数据统计过程的若干研究

统计过程控制(statistical process contor, SPC) 是应用统计方法对过程中的各个阶段进行监控,从而达到改进和保证质量的目的. 本文在一些重要的前沿问题上展开研究, 其中包括profile 数据过程的监控和诊断、监测drift 飘移的控制图、多元过程控制和多阶段过程的检测和诊断. 本文引入并开发各种新的统计技术, 紧密结合计算算法, 解决这些当前质量控制领域研究的难点问题.     

Quality control of wastewater treatment: A new approach

This paper presents a new approach to quality control of wastewater treatment. The first part formulates basic principles of statistical process control (SPC) and Taguchi Method. Then it is shown that the classical SPC technique used in industry, cannot be to applied to wastewater treatment plants without adaptation and that the Taguchi Method is inapplicable in this case. This is followed by an example from literature, which demonstrates the problems of applying the SPC method to wastewater treatment. The third part of the paper presents a case study where the performance of a greywater treatment plant is examined. The performance is analyzed by means of cross-correlation between input and output parameters. A new approach to SPC of wastewater treatment, either “Dynamic SPC” or “linear regression SPC”, is presented, and a permeability coefficient is developed (the ratio of the output and input energies). Both are proposed as monitoring tools for wastewater treatment systems.     

Multivariate Bayesian process control for a finite production run

Most industrial products and processes are characterized by several, typically correlated measurable variables, which jointly describe the product or process quality. Various control charts such as Hotelling’s T², EWMA and CUSUM charts have been developed for multivariate quality control, where the values of the chart parameters, namely the sample size, sampling interval and the control limits are determined to satisfy given economic and/or statistical requirements. It is well known that this traditional non-Bayesian approach to a control chart design is not optimal, but very few results regarding the form of the optimal Bayesian control policy have appeared in the literature, all limited to a univariate chart design. In this paper, we consider a multivariate Bayesian process mean control problem for a finite production run under the assumption that the observations are values of independent, normally distributed vectors of random variables. The problem is formulated in the POMDP (partially observable Markov decision process) framework and the objective is to determine a control policy minimizing the total expected cost. It is proved that under standard operating and cost assumptions the control limit policy is optimal. Cost comparisons with the benchmark chi-squared chart and the MEWMA chart show that the Bayesian chart is highly cost effective, the savings are larger for smaller values of the critical Mahalanobis distance between the in-control and out-of-control process mean.     

Comparison of the cuscore, GLRT and cusum control charts for detecting a dynamic mean change

Although statistical process control (SPC) techniques have been focused mostly on detecting constant mean shifts, dynamic and time-varying process changes frequently occur in the monitoring of feedback-controlled and autocorrelated processes. In this research, the performances of cumulative score (Cuscore), generalized likelihood ratio test (GLRT), and cumulative sum (CUSUM) charts in detecting a dynamic mean change that finally approaches a steady-state value are compared. Theoretical results in average run length (ARL) comparison are provided. From the theretical study we find that, when the steady-state value is greater or less than a critical value,Rδ/2+δ/2, the Cuscore and CUSUM charts have a different performance in detecting the mean change. We prove also that the GLRT has the best performance among the three charts in detecting any mean change for which the steady-state value is not equal to δ or δR, when the in-control ARL is large.     

Environmental statistical process control using an augmented neural network classification approach

Shifts in the values of monitored environmental parameters can help to indicate changes in an underlying system. For example, increased concentrations of copper in water discharged from a manufacturing facility might indicate a problem in the wastewater treatment process. The ability to identify such shifts can lead to early detection of problems and appropriate remedial action, thus reducing the risk of long-term consequences. Statistical process control (SPC) techniques have traditionally been used to identify when process parameters have shifted away from their nominal values. In situations where there are correlations among the observed outputs of the process, however, as in many environmental processes, the underlying assumptions of SPC are violated and alternative approaches such as neural networks become necessary. A neural network approach that incorporates a geometric data preprocessing algorithm and identifies the need for increased sampling of observations was applied to facilitate early detection of shifts in autocorrelated environmental process parameters. Utilization of the preprocessing algorithm and the increased sampling technique enabled the neural network to accurately identify the process state of control. The algorithm was able to identify shifts in the highly correlated process parameters with accuracies ranging from 96.4% to 99.8%.     

自相关过程的质量控制图

常规控制图应用的基本假设是从过程得到的测量值彼此独立,但许多连续型的制造业生产过程(例如化学和制药)往往存在自相关,此时常规控制图容易虚发警报。基于数据的样本自相关函数,本文改进了常规控制图的控制界限,使之适用于自相关过程,并运用常规X-s控制图和本文修正的X控制图对一个实际案例进行了比较分析,结果表明本文修正的X控制图可正确地判断过程是否处于受控状态。     

Integrated planning of transportation and recycling for multiple plants based on process simulation

By-products accrue in all stages of industrial production networks. Legal requirements, shortening of primary resources and their increasing prices make their recycling more and more important. For the re-integration into the economic cycle the scope of common supply chain management is enlarged and so-called closed-loop supply chains with adapted and new planning tasks are developed. In process industries this requires a detailed modelling of the recycling processes. This is of special relevance for operational planning tasks in which an optimal usage of a given production system is envisaged. This contribution presents an integrated planning approach for a real-world case study from the zinc industry to achieve such an adequate process modelling. We consider the planning problem of a company that operates four metallurgical recycling plants and has to allocate residues from different sources to these recycling sites. The allocation determines the raw material mix used in the plants. This blending has an effect on the transportation costs and the costs and revenues of the individual technical processes in the recycling plants. Therefore in this problem transportation and recycling planning for multiple sites have to be regarded in an integrated way. The necessary detailed process modelling is achieved by the use of a flowsheet process simulation system to model each recycling plant individually. The models are used to derive linear input–output functions by multiple linear regression analyses. These are used in an integrated planning model to calculate the decision-relevant input and output flows that are dependent upon the allocation of the residues to the recycling sites. The model is embedded in a decision support system for the operational use. An example application and sensitivity analyses demonstrate and validate the approach and its potentials. The approach is transferable to other recycling processes as well as to other processes in process industries.     

Using economic X control chart design methodology to estimate and optimize machine efficiency in the case of multimachine assignments

Estimation and optimization of machine efficiency in the case of multi machine assignments is a difficult issue for some industries such as textile industry, due to the machine interference. Although the problem is not new, the solutions proposed are not full satisfactory. For example, the analytical formulas are not suitable for high number of machines; queuing theory approaches are complex and impractical for mill managers. On the other hand, simulation methodology seems to be a perfect tool to estimate and analyze; however, the simulation package programs are expensive and are not suitable for systematic optimization. In this paper, Duncan's economic control chart design methodology is proposed as an alternative way for one of the oldest industrial problems. This study shows that this new approach is a more efficient solution to this stochastic problem. Copyright © 2009 John Wiley & Sons, Ltd.     

Impulse and continuous control of piecewise deterministic Markov processes

In this paper we consider the problem of impulse and continuous control on the jump rate and post jump location parameters of piecewise-deterministic Markov processes (PDP's). In a companion paper we studied the optimal stopping with continuous control problem of PDP's assuming only absolutely continuity along trajectories hypothesis on the final cost function. In this paper we apply these results to obtain optimality equations for the impulse and continuous control problem of PDP's in terms of a set of quasi-variational inequalities as well as on the first jump time operator of the process. No continuity or differential assumptions on the whole state space, neither stability assumptions on the parameters of the problem are required. It is shown that if the post intervention operator satisfies some locally lipschitz continuity along trajectories properties then so will the value function of the impulse and continuous control problem.     

两指标双F统计过程控制图

当产品质量指标服从二元正态分时,可用T2控制图与Λ控制图联合判断产品生产的过程是否处于受控状态。本文利用T2统计量与F统计量、Λ统计量与F统计量之间的关系,得到了两指标情形下两类基于F分布统计量的统计过程控制图,简称双F统计过程控制图,并给出了控制图应用实例。     

Economic design of integrated model of control chart and maintenance management

There are two key tools for the control of production processes — Statistical Process Control (SPC) and Maintenance Management (MM), which are traditionally separated (both in science and in business practice), even though their goals overlap a great deal. Their common goal is to achieve optimal product quality, little downtime and cost reduction by controlling variances in the process. Since single or separated parallel applications may not be fully effective, this paper discusses the integration of statistical process control and maintenance, and provides an integrated model of Control Chart (CC) and MM. A mathematical model is given to analyze the cost of the integrated model and the grid-search approach is used to find the optimal values of policy variables (n,h,L,k) that minimize hourly cost. Finally, a numerical experiment is conducted to investigate the effects of cost parameters on the solution of the design.     

ARL计算方法综述

质量控制图的研究在统计过程控制(Statistical Process Control,简称SPC)中占有很重要的位置,且在实际中得到了很好的应用,也取得了很好的经济效益和社会效益。而ARL(AverageRun Length)和ATS(Average Time to Signal)分别是评价各种静态和动态控制图性质好坏的一个常用的关键指标。到现在为止,关于各种控制图ARL或ATS的计算大致有三种方法:马氏链法、积分方程法和随机模拟,本文将针对各种不同的控制图,对上述前两种方法做一个综述性的总结,以方便研究者和实际工作参考使用。     

Estimating the limits for statistical process control charts: A direct method improving upon the bootstrap

This paper considers the problem of finding limits for a statistical process control (SPC) chart for the process mean, when the process distribution is unknown. The bootstrap method estimates these limits relying on Monte Carlo methods, which are subject to simulation errors. Therefore this paper develops a computationally efficient enumeration method for exact calculations of the control limits.     

Hamilton-Jacobi-Bellman inequality for the average control of piecewise deterministic Markov processes

ABSTRACT

The main goal of this paper is to study the infinite-horizon long run average continuous-time optimal control problem of piecewise deterministic Markov processes (PDMPs) with the control acting continuously on the jump intensity λ and on the transition measure Q of the process. We provide conditions for the existence of a solution to an integro-differential optimality inequality, the so called Hamilton-Jacobi-Bellman (HJB) equation, and for the existence of a deterministic stationary optimal policy. These results are obtained by using the so-called vanishing discount approach, under some continuity and compactness assumptions on the parameters of the problem, as well as some non-explosive conditions for the process.     

A linear quadratic control problem for the stochastic heat equation driven by Q-Wiener processes

We consider a control problem for the stochastic heat equation with Neumann boundary condition, where controls and noise terms are defined inside the domain as well as on the boundary. The noise terms are given by independent Q-Wiener processes. Under some assumptions, we derive necessary and sufficient optimality conditions stochastic controls have to satisfy. Using these optimality conditions, we establish explicit formulas with the result that stochastic optimal controls are given by feedback controls. This is an important conclusion to ensure that the controls are adapted to a certain filtration. Therefore, the state is an adapted process as well.     

Optimization designs and performance comparison of two CUSUM schemes for monitoring process shifts in mean and variance

In statistical process control (SPC), when dealing with a quality characteristic x that is a variable, it is usually necessary to monitor both the mean value and variability. This article proposes an optimization algorithm (called the holistic algorithm) to design the CUSUM charts for this purpose. It facilitates the determination of the charting parameters of the CUSUM charts and considerably or significantly increases their overall detection effectiveness. A single CUSUM chart (called the ABS CUSUM chart) has been developed by the holistic algorithm and fully investigated. This chart is able to detect two-sided mean shifts and increasing variance shifts by inspecting the absolute value of sample mean shift. The results of performance studies show that the overall performance of the ABS CUSUM chart is nearly as good as an optimal 3-CUSUM scheme (a scheme incorporating three individual CUSUM charts). However, since the ABS CUSUM chart is easier for implementation and design, it may be more suitable for many SPC applications in which both mean and variance of a variable have to be monitored.     

A statistical process control approach to selecting a warm-up period for a discrete-event simulation

The selection of a warm-up period for a discrete-event simulation continues to be problematic. A variety of selection methods have been devised, and are briefly reviewed. It is apparent that no one method can be recommended above any other. A new approach, based upon the principles of statistical process control, is described (SPC method). Because simulation output data are often highly autocorrelated and potentially non-normal, the batch means method is employed in constructing the control chart. The SPC method is tested on seven data sets and encouraging results are obtained concerning its accuracy. The approach is also discussed with respect to its ease of implementation, simplicity, generality of use and requirements for parameter estimation.     

Continuous Markovian Model for Unexpected Shift in SPC

Various process models for discrete manufacturing systems (parts industry) can be treated as bounded discrete-space Markov chains, completely characterized by the original in-control state and a transition matrix for shifts to an out-of-control state. The present work extends these models by using a continuous-state Markov chain, incorporating non-random corrective actions. These actions are to be realized according to the statistical process control (SPC) technique and should substantially affect the model. The developed stochastic model yields Laplace distribution of a process mean. Real-data tests confirm its applicability for the parts industry and show that the distribution parameter is mainly controlled by the SPC sample size.     

An ewma-cuscore quality control procedure for process variability

Combined Shewhart-cumulative score (cuscore) quality control schemes are available for controlling the mean of a continuous production process. In many industrial applications, it is important to control the process variability as well. The proposed exponentially weighted moving average combined Shewhart-cumulative score ewma-cuscore quality control procedure for detecting shifts in process variability uses the procedures developed by Ncube and Woodall [1] to monitor shifts in the process mean of continuous production processes. It is shown, in the one-sided case, by Average Run Length (ARL) comparisons, that the proposed scheme performs significantly and uniformly better than comparative standard cuscore procedures for shifts in the process standard deviation values considered.