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 f...

statistical process control (spc) charts play a major role in quality control systems, and their correct interpretation leads to discovering probable irregularities and errors of the production system. in this regard, various artificial neural networks have been developed to identify mainly singular patterns of spc charts, while having drawbacks in handling multiple concurrent patterns. in this...

Control charts are graphical tools widely used to monitor manufacturing processes to quickly detect any change in a process that may result in a change in product quality. The statistic plotted on a control chart is based on samples of n ≥ 1 observations (rational subgroups) that may be taken at regular sampling intervals. However, there are numerous practical applications using individual obse...

In such processes as wafer-grinding and LPCVD, the variation within a group of measurements is traceable to various causes, and therefore, needs to be decomposed into relevant components of variation for an effective equipment monitoring and diagnosis. In this article, an LPCVD process is considered as an example, and control charting methods are developed for monitoring various fixed and rando...

As part of the investigations into the crimes of Harold Shipman, it has become clear that there is little monitoring of deaths in general practice. By use of data on annual deaths at family physician and practice level for five English health authorities for 1993-99, we investigate whether cumulative sum charts (a type of statistical process control chart) could be used to create a workable mon...

In the manufacture of metal fasteners in a progressive die operation, and other industrial situations, important quality dimensions cannot be measured on a continuous scale, and parts are classified into groups using a step gauge. This article proposes a version of exponentially weighted moving average (EWMA) control charts applicable to monitoring the grouped data for process shifts. The run l...

This paper illustrates how Phase I estimators in statistical process control (SPC) can affect the performance of Phase II control charts. The deleterious impact of poor Phase I estimators on the performance of Phase II control charts is illustrated in the context of profile monitoring. Two types of Phase I estimators are discussed. One approach uses functional cluster analysis to initially dist...

The monitoring, fault detection and visualization of defects are a strategic issue for product quality. This paper presents a novel methodology based on the integration of textural Multivariate image analysis (MIA) and multivariate statistical process control (MSPC) for process monitoring. The proposed approach combines MIA and p-control charts, as well as T 2 and RSS images for defect location...

In an attempt to improve the procedures for statistical process control many researchers have developed and proposed a variety of adaptive control charts in the last decade. The common characteristic of those charts is that one or more of the chart parameters (sampling interval, sample size, control limits) is allowed to change during operation taking into account current sample information. Du...

Detecting dynamic mean shifts is particularly important in monitoring feedbackcontrolled processes in which time-varying shifts are usually observed. When multivariate control charts are being utilized, one way to improve performance is to reduce dimensions. However, it is difficult to identify and remove non-informative variables statically in a process with dynamic shifts, as the contribution...