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, ...

In this paper Principal Components Analysis (PCA) is used for detecting faults in a simulated wastewater treatment plant (WWTP). PCA is a multivariate statistical technique used in multivariate statistical process control (MSPC) and fault detection and isolation (FDI) perspectives. PCA reduces the dimensionality of the original historical data by projecting it onto a lower dimensionality space....

This paper develops a new distribution-free multivariate procedure for statistical process control based on minimal spanning tree (MST), which integrates a multivariate two-sample goodness-of-fit (GOF) test based on MST and change-point model. Simulation results show that our proposed procedure is quite robust to nonnormally distributed data, and moreover, it is efficient in detecting process s...

In this work, a combination between Multivariate Statistical Process Control (MSPC) and an automatic classification algorithm is developed to application in Waste Water Treatment Plant. Multiway Principal Component Analysis is used as MSPC method. The goal is to create a model that describes the batch direction and helps to fix the limits used to determine abnormal situations. Then, an automati...

In this work, a combination between Multivariate Statistical Process Control (MSPC) and an automatic classification algorithm is developed to application in Waste Water Treatment Plant. Multiway Principal Component Analysis is used as MSPC method. The goal is to create a model that describes the batch direction and helps to fix the limits used to determine abnormal situations. Then, an automati...

Univariate and multivariate statistical process control (USPC and MSPC) methods have been widely used in process industries for fault detection. However, their practicability and achievable performance are limited due to the assumptions that a process is operated in a steady state and that variables are normally distributed. In the present work, external analysis is proposed to distinguish from...

Several extensions are made to the theory of multivariate process monitoring via Principal Components Analysis (PCA). An important robustness issue is addressed: the continued use of the PCA model after detection of a sensor failure. Without some adjustment, a single failed sensor can obscure other failures, thus rendering the monitoring method useless. It is shown here that one can calculate a...

Batch and semi-batch modes of production are common in a number of high value-added industries including specialty chemicals, pharmaceuticals, and biologics. Online monitoring of such processes seeks to detect run-to-run deviations, anomalies or faulty conditions by analyzing real-time measurements so that they can be corrected quickly and the batch recovered. Multivariate statistical methods h...

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...