Conventional Statistical Process Control Techniques Research Articles

Variable Selection‐based Multivariate Cumulative Sum Control Chart

High‐dimensional applications pose a significant challenge to the capability of conventional statistical process control techniques in detecting abnormal changes in process parameters. These techniques fail to recognize out‐of‐control signals and locate the root causes of faults especially when small shifts occur in high‐dimensional variables under the sparsity assumption of process mean changes. In this paper, we propose a variable selection‐based multivariate cumulative sum (VS‐MCUSUM) chart for enhancing sensitivity to out‐of‐control conditions in high‐dimensional processes. While other existing charts with variable selection techniques tend to show weak performances in detecting small shifts in process parameters due to the misidentification of the ‘faulty’ parameters, the proposed chart performs well for small process shifts in identifying the parameters. The performance of the VS‐MCUSUM chart under different combinations of design parameters is compared with the conventional MCUSUM and the VS‐multivariate exponentially weighted moving average control charts. Finally, a case study is presented as a real‐life example to illustrate the operational procedures of the proposed chart. Both the simulation and numerical studies show the superior performance of the proposed chart in detecting mean shift in multivariate processes. Copyright © 2016 John Wiley & Sons, Ltd.

Improved design of kernel distance–based charts using support vector methods

Statistical Process Control (SPC) techniques that originated in manufacturing have also been used to monitoring the quality of various service processes, which can be characterized by one or several variables. In the literature, these variables are usually assumed to be either continuous or categorical. However, in reality, the quality characteristics of a service process may include both continuous and categorical variables (i.e., mixed-type variables). Direct application of conventional SPC techniques to monitor such mixed-type variables may cause increased false alarm rates and misleading conclusions. One promising solution is the kernel distance–based chart (K-chart), which makes use of Support Vector Machine (SVM) methods and requires no assumption on the variable distribution. This article provides an improved design of the SVM-based K-chart. A systematic approach to parameter selection for the considered charts is provided. An illustration and comparison are presented based on a real example from a logistics firm. The results confirm the improved performance obtained by using the proposed design scheme.

A density-based statistical process control scheme for high-dimensional and mixed-type observations

Statistical Process Control (SPC) techniques are useful tools for detecting changes in process variables. The structure of process variables has become increasingly complex as a result of increasingly complex technologies. The number of variables is usually large and categorical variables may appear alongside continuous variables. Such observations are considered to be high-dimensional and mixed-type observations. Conventional SPC techniques may lose their accuracy and efficiency in detecting changes in a process with high-dimensional and mixed-type observations. This article presents a density-based SPC approach, which is derived from a Local Outlier Factor (LOF) scheme, as a solution to this problem. The parameters in an LOF scheme are investigated and a procedure to design a corresponding control chart is presented. The good performance of the proposed control scheme is demonstrated via numerical simulation.

Autocorrelated SPC for Non-Normal Situations

The importance of statistical process control (SPC) techniques in quality improvement is well recognized in industry. However, most conventional SPC techniques have been developed under the assumption of independent, identically and normally distributed observations. With advances in sensing and data capturing technologies, large volumes of data are being routinely collected from individual units in manufacturing industries. These data are often autocorrelated and skewed. Conventional SPC techniques can lead to false alarms or other types of poor performance monitoring of such data. There is a great need for process control techniques for variation reduction in these environments. Much recent research has focused on the development of appropriate SPC techniques for autocorrelated data, but few studies have considered the impact of non-normality on these techniques. This paper investigates the effect of skewness on conventional autocorrelated SPC techniques, and provides an effective approach based on a scaled weighted variance approach to improve SPC performance in such an environment. Copyright © 2005 John Wiley & Sons, Ltd.

Sufficient statistics process control:An empirical Bayes approach to process control

SUMMARY High volume, highly automated, information intensive, short cycle manufacturing systems severely tax most conventional statistical process control techniques. To meet this new manufacturing domain's control requirements, a new approach is needed. This paper presents such a process control procedure, sufficient statistics process control (SSPC). By drawing on empirical Bayes techniques, SSPC models the time sequence of the process while simultaneously reducing to a few sufficient statistics the large volume of incoming data. As a result, it provides real time, on-line quality control. The paper discusses the conceptual and mathematical foundations for SSPC. Its operation is illustrated through an example. Finally, the paper concludes with a discussion of the limitations of SSPC.