We present a new CuSum chart for monitoring shifts in the mean of autocorrelated data. The monitoring statistic is the plain cumulative sum of differences between the observations and the target value, and the control limits are a function of the asymptotic variance constant and target in-control average run length. Our method uses a variety of non-parametric techniques that are popular in the ...

Anomaly detection (AD) for times series data using the generative adversarial network (GAN) has been proposed in recent years. According to previous study, GAN-based AD outperformed cumulative sum (CUSUM) chart. However, no framework comparison is provided their works. So, we conduct new studies crucial methods (the MAD-GAN and TAnoGAN). First, propose a fair systematic comparisons prediction p...

This paper considers statistical process control (SPC) of univariate processes, and tries to make two contributions to the univariate SPC problem. First, we propose a continuously variable sampling scheme, based on a quantitative measure of the likelihood of a process distributional shift at each observation time point, provided by the p-value of the conventional cumulative sum (CUSUM) charting...

This paper considers statistical process control (SPC) of univariate processes, and tries to make two contributions to the univariate SPC problem. First, we propose a continuously variable sampling scheme, based on a quantitative measure of the likelihood of a process distributional shift at each observation time point, provided by the p-value of the conventional cumulative sum (CUSUM) charting...

In this paper, we evaluate the capability to detect traffic anomalieswith Shewhart, CUSUM, andEWMA control charts. In order to cope with seasonal variation and serial correlation, control charts are not applied to traffic measurement time-series directly, but to the prediction errors of exponential smoothing and Holt-Winters forecasting. The evaluation relies on flow data collected in an ISP ba...

BACKGROUND The lack of robust systems for monitoring quality in healthcare has been highlighted. Statistical process control (SPC) methods, utilizing the increasingly available routinely collected electronic patient records, could be used in creating surveillance systems that could lead to rapid detection of periods of deteriorating standards. We aimed to develop and test a CUmulative SUM (CUSU...

This paper analyses and evaluates the properties of a CUSUM chart designed for monitoring the process mean in short production runs. Several statistical measures of performance that are appropriate when the process operates for a finite-time horizon are proposed. The methodology developed in this paper can be used to evaluate the performance of the CUSUM scheme for any given set of chart parame...

Statistical process control (SPC) has been used to achieve and maintain control of various processes in industry (Stoumbos, Reynolds, Ryan, and Woodall 2000). The control chart is a primary SPC tool to monitor process variability and promote quality improvement by means of detecting process shifts requiring corrective actions. As a graphical monitor, control charts generally contain a centerlin...

The sensitivity of four tests on Shewhart type Q-eharis and of specially designed EWMA and CUSUM Q-eharts to detect one-step permanent shifts of either a normal mean or standard deviation has been studied. The usual test that signals for one point beyond three standard deviations on a Shewhart chart is found to have poor sensitivity, generally. The test that signals when four out of five consec...

The recent blooming developments of Artificial Intelligence (AI), Internet Things (IoT), and Data Science (DS) have put Smart Manufacturing (SM) into a new context. This leads to more attractions on control charts as one the useful tools that contribute success in SM by anomaly detection (AD) approach. Coefficient variation (CV) is popular statistic used quality SM. In this paper, we propose in...