Information from on-line imaging sensors has great potential for the monitoring and control of quality in spatially distributed systems. The major difficulty lies in the efficient extraction of information from the images, information such as the frequencies of occurrence of specific and often subtle features, and their locations in the product or process space. This paper presents an overview ...

For signalling alarms sooner when the dispersion of a multivariate process is “increased”, a multivariate control chart for Phase II process monitoring is proposed as a supplementary tool to the usual monitoring schemes designed for detecting general changes in the covariance matrix. The proposed chart is constructed based on the one-sided likelihood ratio test (LRT) for testing the hypothesis ...

A nonparametric Bayesian model is proposed for segmenting time-evolving multivariate spatial point process data. An inhomogeneous Poisson process is assumed, with a logistic stick-breaking process (LSBP) used to encourage piecewise-constant spatial Poisson intensities. The LSBP explicitly favors spatially contiguous segments, and infers the number of segments based on the observed data. The tem...

This article investigates multivariate spatial process models suitable for predicting multiple forest attributes using a multisource forest inventory approach. Such data settings involve several spatially dependent response variables arising in each location. Not only does each variable vary across space, they are likely to be correlated among themselves. Traditional approaches have attempted t...

Consider a set of multivariable input/output process data. Given a new observation we ask the following questions: is the new observation normal or abnormal? is one of the inputs or outputs abnormal (faulty) and which? Assuming a linear regression model of the process, the problem is solved through Bayesian hypothesis testing. The proposed formulation differs from existing multivariable statist...

In this paper, we study a method to construct a multivariate counting process with positive dependencies between the event occurrences. Conditional on a random effect with a positive stable distribution, the univariate counting processes are independent non-homogeneous Poisson processes with a power intensity function. The applicability of the model is illustrated in three examples: a horse rac...

The primary objective of this note is to reduce the false alarms in multivariate statistical process control (MSPC). The issue of false alarms is inherent within MSPC as a result of the definition of control limits. It has been observed that under normal operating conditions, the occurrence of “outof-control” data, i.e. false alarms, conforms to a Bernoulli distribution. Therefore this issue ca...

Classical techniques for analysing multivariate extremes can often be framed in terms of the point process representation of de Haan (1985). Amongst other things, this representation provides a characterisation of the limiting distribution of the normalised componentwise maxima of independent and identically distributed unit Fréchet variables, i.e. the class of multivariate extreme value distri...

Multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance are becoming more important because of the availability of on-line process computers which routinely collect measurements on large numbers of process variables. Traditional univariate control charts have been extended to multivariate quality control situations using the Hotelling T2 stat...

In this paper, a modified independent component analysis (ICA) and its application to process monitoring are proposed. The basic idea of this approach is to use the modified ICA to extract some dominant independent components from normal operating process data and to combine them with statistical process monitoring techniques. The proposed monitoring method is applied to fault detection and ide...