Latent Structures-Based Multivariate Statistical Process Control: A Paradigm Shift

ABSTRACT

The basic fundamentals of statistical process control (SPC) were proposed by Walter Shewhart for data-starved production environments typical in the 1920s and 1930s. In the 21st century, the traditional scarcity of data has given way to a data-rich environment typical of highly automated and computerized modern processes. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio, multistage and multiway structures, and missing values. Conventional univariate and multivariate SPC techniques are not suitable in these environments. This article discusses the paradigm shift to which those working in the quality improvement field should pay keen attention. We advocate the use of latent structure–based multivariate statistical process control methods as efficient quality improvement tools in these massive data contexts. This is a strategic issue for industrial success in the tremendously competitive global market.     

A spatial variable selection method for monitoring product surface

Two-dimensional (2-D) data maps are generated in certain advanced manufacturing processes. Such maps contain rich information about process variation and product quality status. As a proven effective quality control technique, statistical process control (SPC) has been widely used in different processes for shift detection and assignable cause identification. However, charting algorithms for 2-D data maps are still vacant. This paper proposes a variable selection-based SPC method for monitoring 2-D wafer surface. The fused LASSO algorithm is firstly employed to identify potentially shifted sites on the surface; a charting statistic is then developed to detect statistically significant shifts. As the variable selection algorithm can nicely preserve shift patterns in spatial clusters, the newly proposed chart is proved to be both effective in detecting shifts and capable of providing diagnostic information for process improvement. Extensive Monte Carlo simulations and a real example have been used to demonstrate the effectiveness and usage of the proposed method.     

基于改进田口方法的轮毂装配过程质量控制研究

针对风力机轮毂装配工序的质量控制问题，结合多品种小批量生产模式的特点，提出了一种面向定位布置的轮毂装配关键工序识别与监控的方法。综合考虑轮毂装配生产中的成本因素及相对质量损失，制定装配工序的质量特性损失标准，采用改进的质量损失函数识别轮毂装配关键工序；为解决统计过程控制（SPC）方法应用于小批量生产模式的瓶颈，引入基于直觉模糊集的工序相似性分析与评定方法，通过分析工序相似性的影响因素，构建轮毂装配成组工序相似性评判指标体系及评定模型，完成工序相似性的测度。最终使用SPC方法监控管理轮毂装配关键工序，确保轮毂的装配质量。该研究为小批量生产模式中运用SPC方法管理过程质量提供了理论依据。     

The funnel experiment: The Markov‐based SPC approach

The classical funnel experiment was used by Deming to promote the idea of statistical process control (SPC). The popular example illustrates that the implementation of simple feedback rules to stationary processes violates the independence assumption and prevents the implementation of conventional SPC. However, Deming did not indicate how to implement SPC in the presence of such feedback rules. This pedagogical gap is addressed here by introducing a simple feedback rule to the funnel example that results in a nonlinear process to which the traditional SPC methods cannot be applied. The proposed method of Markov‐based SPC, which is a simplified version of the context‐based SPC method, is shown to monitor the modified process well. Copyright © 2007 John Wiley & Sons, Ltd.     

Economic cost models of integrated APC controlled SPC charts

In this paper, an economic cost model is proposed for processes integrating both automatic process control (APC) and statistical process control (SPC) for quality monitoring and control. Both the special cause and common cause variations are reduced by applying integrated APC and SPC. Traditionally, the integrated processes using APC and SPC are evaluated by the average run length (ARL). However, ARL may not be appropriate as a measurement of the economic design since it does not take into consideration the run length variation. Also, there are few studies that compare the cost models of such an integrated control system and the effect of cost parameters using different APC controllers. Therefore, we develop an economic cost model using non-homogenous Poisson process to describe the occurrence of an APC adjustment and develop a long run expected cost to investigate the use of different controllers in such integrated systems. Numerical examples are presented to demonstrate the applicability of the proposed model.     

An approach of fuzzy logic evaluation and control in SPC

Quality control plays an important part in most industrial systems. Its role in providing relevant and timely data to management for decision‐making purposes is vital. A method that uses statistical techniques to monitor and control product quality is called statistical process control (SPC), where control charts are test tools frequently used for monitoring the manufacturing process. Engineers or managers can evaluate an abnormal process by using SPC zone rules in control charts. In the conventional use of the zone rules the user is only able to determine whether or not the process is out of control. What action should be taken to adjust the process is uncertain and is evaluated based on knowledge of the system and past experiences. This paper explores the integration of fuzzy logic and control charts to create and design a fuzzy–SPC evaluation and control (FSEC) method based on the application of fuzzy logic to the SPC zone rules. A simulation program implementing FSEC was written in Borland C++ 5.0 and simulation results were obtained and analysed. The abnormal processes simulated were automatically adjusted for each of the zone rules tested and showed an improved performance after the control action, thus confirming the merit of the technique as a special method with the specific numerical control action based on a quality evaluation criterion. Copyright © 2000 John Wiley & Sons, Ltd.     

一种保证预设质量和过程稳态的SPC方法

提出三种过程质量指数(PQI)的过程质量指数系统,基于过程质量指数的统计公差提供了一个过程质量要求和控制图设计之间的标准化界面.通过基于过程质量指数的统计公差带增加对x--R或x--s控制图中线的约束,建立一种保证预设质量和过程稳态的统计过程控制新方法.这不仅增强了控制图的功能,也为过程质量规划、统计公差和保证预设质量的SPC相关参数的并行设计提供了指导.     

Automated process control and quality engineering for processes with damped controllers

Statistical process control (SPC) is a statistical based approach that monitors long-term process performance. It is used to identify and remove causes of process disturbances. Automatic process control (APC) is an engineering based approach that monitors processes and provides short-term adjustments. It is used to continually compensate for process disturbances. This paper focuses on control systems that combine these two approaches and may provide improved process performance when compared with either approach. The objective is to investigate the conditions at which SPC or APC or both are applicable as well as the control limits that minimize the total quality loss.     

Using an MQE chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes

Control charts are recognized as one of the most important tools for statistical process control (SPC), used for monitoring any abnormal deviations in the state of manufacturing processes. However, the effectiveness of control charts is strictly dependent on statistical assumptions that in real applications are frequently violated. In contrast, neural networks (NNs) have excellent noise tolerance in real time, requiring no hypothesis on the statistical distribution of monitored processes. This feature makes NNs promising tools for quality control. In this paper, a self-organizing map (SOM)-based monitoring approach is proposed for enhancing the monitoring of processes. It is capable of providing a comprehensive and quantitative assessment value for the current process state, achieved by minimum quantization error (MQE) calculation. Based on MQE values over time series, a novel MQE chart is developed for monitoring process changes. The aim of this research is to analyse the performance of the MQE chart under the assumption that predictable abnormal patterns are not available. To this aim, the performance of the MQE chart in manufacturing processes (including non-correlated, auto-correlated and multivariate processes) is evaluated. The results indicate that the MQE chart may be a promising tool for quality control.     

Using Statistical Thinking and Designed Experiments to Understand Process Operation

Using a series of four case studies, this article illustrates the integration of statistical process control and designed experiments. For such an integration to be effective, this article points out the need to use statistical process control (SPC) as a tool for active process study, rather than simply as a method for maintaining and controlling processes. The use of SPC in this fashion is also illustrated throughout the case studies.     

LRProb control chart based on logistic regression for monitoring mean shifts of auto-correlated manufacturing processes

With the growth of automation in manufacturing, process quality characteristics are being measured at higher rates and data is more likely to be auto-correlated. Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because process parameters are highly auto-correlated. Several attempts such as some time series based control charts have been made in the previous years to extend traditional SPC techniques. However, these extensions pose some serious limitations for monitoring the process mean shifts. These charts require that a suitable model has been identified for the time series of process observations before residuals can be obtained. In this paper, a logistic regression (LR)-based process monitoring model is proposed for enhancing the monitoring of processes. It is capable of providing a comprehensible and quantitative assessment value for the current process state, which is achieved by the event occurrence probability calculation of LR. Based on these probability values over the time series, a novel chart: LRProb chart, is developed for monitoring and visualising process changes. The aim of this research is to analyse the performance of the LRProb chart under the assumption that only a small number of predictable abnormal patterns are available. To such aim, the performance of the LRProb chart is evaluated on two real-world industrial cases and simulated processes. Given the simplicity, visualisation and quantification of the proposed LRProb chart, this approach is proved from the experiments to be a feasible alternative for quality monitoring in the case of auto-correlated process data.     

Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process

In modern manufacturing processes, massive amounts of multivariate data are routinely collected through automated in-process sensing. These data often exhibit high correlation, rank deficiency, low signal-to-noise ratio and missing values. Conventional univariate and multivariate statistical process control techniques are not suitable to be used in these environments. This article discusses these issues and advocates the use of multivariate statistical process control based on principal component analysis (MSPC-PCA) as an efficient statistical tool for process understanding, monitoring and diagnosing assignable causes for special events in these contexts. Data from an autobody assembly process are used to illustrate the practical benefits of using MSPC-PCA rather than conventional SPC in manufacturing processes.     

The revised Tennessee Eastman process simulator as testbed for SPC and DoE methods

Engineering process control and high-dimensional, time-dependent data present great methodological challenges when applying statistical process control (SPC) and design of experiments (DoE) in continuous industrial processes. Process simulators with an ability to mimic these challenges are instrumental in research and education. This article focuses on the revised Tennessee Eastman process simulator providing guidelines for its use as a testbed for SPC and DoE methods. We provide flowcharts that can support new users to get started in the Simulink/Matlab framework, and illustrate how to run stochastic simulations for SPC and DoE applications using the Tennessee Eastman process.     

Process monitoring with ICA‐based signal extraction technique and CART approach

In order to reduce the variation in a manufacturing process, traditional statistical process control (SPC) techniques are the most frequently used tools in monitoring engineering process control (EPC)‐controlled processes for detecting assignable cause process variation. Even though application of SPC with EPC can successfully detect time points when abnormalities occur during process, their combination can also cause an increased occurrence of false alarms when autocorrelation is present in the process. In this paper, we propose an independent component analysis‐based signal extraction technique with classification and regression tree approach to identify disturbance levels in the correlated process parameters. For comparison, traditional cumulative sum (CUSUM) chart was constructed to evaluate the identifying capability of the proposed approach. The experimental results show that the proposed method outperforms CUSUM control chart in most instances. Copyright © 2008 John Wiley & Sons, Ltd.     

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.     

Wavelet-based SPC procedure for complicated functional data

Functional data characterize the quality or reliability performance of many manufacturing processes. As can be seen in the literature, such data are informative in process monitoring and control for nanomachining, for ultra-thin semiconductor fabrication, and for antenna, steel-stamping, or chemical manufacturing processes. Many functional data in manufacturing applications show complicated transient patterns such as peaks representing important process characteristics. Wavelet transforms are popular in the computing and engineering fields for handling these types of complicated functional data. This article develops a wavelet-based statistical process control (SPC) procedure for detecting ‘out-of-control’ events that signal process abnormalities. Simulation-based evaluations of average run length indicate that our new procedure performs better than extensions from well-known methods in the literature. More importantly, unlike recent SPC research on linear profile data for monitoring global changes of data patterns, our methods focus on local changes in data segments. In contrast to most of the SPC procedures developed for detecting a known type of process change, our idea of updating the selected parameters adaptively can handle many types of process changes whether known or unknown. Finally, due to the data-reduction efficiency of wavelet thresholding, our procedure can deal effectively with large data sets.     

A wavelet‐based nonparametric CUSUM control chart for autocorrelated processes with applications to network surveillance

Statistical process control (SPC) has natural applications in data network surveillance. However, network data are commonly autocorrelated, which presents challenges to the basic SPC methods. Most existing SPC methods for correlated data assume parametric models to account for the correlation structure within the data. Those model assumptions can be difficult to justify in practice. In this paper, we propose a nonparametric cumulative sum (CUSUM) control chart for autocorrelated processes. In our proposed approach, we incorporate a wavelet decomposition and a nonparametric multivariate CUSUM control chart to obtain a robust procedure for autocorrelated processes without distribution assumptions. Extensive simulations show that the procedure appropriately controls the in‐control average run length and also has good sensitivity for detecting location shifts.     

Context-Based Statistical Process Control

Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and state-dependent processes. This article introduces the context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The first is a reference tree that represents the “in control” reference behavior of the process; the second is a monitored tree, generated periodically from a sample of sequenced observations, that represents the behavior of the process at that period. The Kullback–Leibler (KL) statistic is used to measure the relative “distance” between these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic indicates whether there has been any significant change in the process that requires intervention. An example of buffer-level monitoring in a production system demonstrates the viability of the new method with respect to conventional methods.     

Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

In modern industries, advanced imaging technology has been more and more invested to cope with the ever‐increasing complexity of systems, to improve the visibility of information and enhance operational quality and integrity. As a result, large amounts of imaging data are readily available. This presents great challenges on the state‐of‐the‐art practices in process monitoring and quality control. Conventional statistical process control (SPC) focuses on key characteristics of the product or process and is rather limited to handle complex structures of high‐dimensional imaging data. New SPC methods and tools are urgently needed to extract useful information from in situ image profiles for process monitoring and quality control. In this study, we developed a novel dynamic network scheme to represent, model, and control time‐varying image profiles. Potts model Hamiltonian approach is introduced to characterize community patterns and organizational behaviors in the dynamic network. Further, new statistics are extracted from network communities to characterize and quantify dynamic structures of image profiles. Finally, we design and develop a new control chart, namely, network‐generalized likelihood ratio chart, to detect the change point of the underlying dynamics of complex processes. The proposed methodology is implemented and evaluated for real‐world applications in ultraprecision machining and biomanufacturing processes. Experimental results show that the proposed approach effectively characterize and monitor the variations in complex structures of time‐varying image data. The new dynamic network SPC method is shown to have strong potentials for general applications in a diverse set of domains with in situ imaging data.     

Nonlinear Profile Monitoring of Reflow Process Data Based on the Sum of Sine Functions

In most statistical process control (SPC) applications, it is often assumed that the quality of a process or product can be adequately represented by the distribution of a univariate quality characteristic. However, in some particular situations, the quality‐related response of interest is not a single variable but a function of some independent variables. Such a functional relationship is called a profile. Recently, profile monitoring has drawn considerable attention in the statistical process control literature. This article proposes a new approach for the reflow process data, which applies the sum of sine functions to model the nonlinear profiles and then the vector of parameter estimates is monitored using the Hotelling T² and metric control charts. Through an actual data set of the reflow process, the proposed approach is compared with the polynomial regression approach in phase I and phase II analyses. The experimental results show that the proposed approach demonstrates good abilities to detect outlying profiles in phase I and provides better out‐of‐control average run length performances than the polynomial regression approach in phase II. Copyright © 2012 John Wiley & Sons, Ltd.