Control of wellbore pressure during drilling operations has always been important in the oil industry as this can prevent the possibility of well blowout. The present research employs a combination of automatic process control and statistical process control for the first time for the identification, monitoring, and control of both random and special causes in drilling operations. To this end, ...

We consider the problem of monitoring variability of autocorrelated processes. This paper combines variance estimation techniques from simulation literature with two statistical process control charts from statistical process control (SPC) literature. The proposed SPC methods do not require any assumptions on the distribution of the underlying process and use a variance estimate from each batch...

Control charts such as X and R charts are widely used in industry to monitor quality. These charts are effective in detecting large departures from the in-control condition. Other process monitoring methods, such as Cumulative Sum (CUSUM) charts are sequential in nature as they accumulate information from previous observations. CUSUM charts are good at detecting more moderate presistent process...

although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. identifying the real time of the process changes is known as change-point estimation problem. there are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. in this paper we propose a novel appro...

When unsafe conditions arise in a workplace, they may result in employee accidents and fatalities. However, if these problems are detected early, new hazard controls and safety initiatives can be introduced in order to actively reduce or prevent the occurrence of these events. Unfortunately, many safety systems currently monitor and report data that has been aggregated over long time periods, m...

OBJECTIVE Applying statistical process control (SPC) charts in perinatal mortality surveillance (as an epidemiological indicator). METHOD The control charts were prepared using 51,840 births; 286 cases were produced from these births. All information came from SUSALUD between January 2004 and December 2007. Two control charts are presented; the first one's central line was considered to be th...

Multivariate control charts are very effective when monitoring several correlated characteristics. Proper process control consists of monitoring both mean and variability. In multivariate settings, the multivariate exponentially weighted moving average (MEWMA) is ideal to monitor the mean vector. Similarly, the recently-proposed multivariate exponentially weighted moving covariance matrix (MEWM...

Excessive variation in a manufacturing process is one of the major causes of a high defect rate and poor product quality. Therefore, quick detection of changes, especially increases in process variability, is essential for quality control. In modern manufacturing environments, most of the quality characteristics that have to be closely monitored are multivariate by the nature of the application...