When monitoring a process which has multivariate normal variables, the Shewhart-type control chart (Hotelling (1947)) traditionally used for monitoring the process mean vector is effective for detecting large shifts, but for detecting small shifts it is more effective to use the multivariate exponentially weighted moving average (MEWMA) control chart proposed by Lowry et al. (1992). It has been...

Traditional control charts are commonly based on the averages of the inspected groups of observations. It turns out to be quite worthwhile to consider alternative approaches. In particular, a very good proposal is to use instead the group minimum for comparison to some suitable upper limit (and likewise the group maximum for comparison to a lower limit). The power of detection during Out-of-Con...

The main purpose of this article is the development and the study of runs rules applied to a Shewhart type control chart for the mean in order to detect non-random patterns in Phase I, when we are interested to conclude if the process is in a stable condition for the first twenty five or thirty samples in order to estimate the process’ parameters. A characterization scheme such as which rule is...

The application of control charts for monitoring financial processes has received a greater focus after recent global crisis. The Generelized AutoRegressive Conditional Heteroskedasticity (GARCH) time series model is widely applied for modelling financial processes. Therefore, traditional Shewhart control chart is developed to monitor GARCH processes. There are some difficulties in financial su...

In recent years the importance of quality has become increasingly apparent due to global competition. According to Banker et al. (1998), quality is the most important strategic issue of top management. It is primarily concerned with consumer’s view of value desired and value received. In general, the term quality addresses two aspects: quality of design and quality of conformance. In the minera...

This paper presents an on-line Statistical Process Control (SPC) technique, based on a Generalized Likelihood Ratio Test (GLRT), for detecting and estimating mean shifts in autocorrelated processes that follow a normally distributed Autoregressive Integrated Moving Average (ARIMA) model. The GLRT is applied to the uncorrelated residuals of the appropriate time-series model. The performance of t...

Accurate and effective anomaly detection and diagnosis of modern engineering systems by monitoring processes ensure reliability and safety of a product while maintaining desired quality. In this paper, an innovative method based on Kullback-Leibler divergence for detecting incipient anomalies in highly correlated multivariate data is presented. We use a partial least square (PLS) method as a mo...