Online monitoring of the multivariate coefficient variation (MCV) can be interest in many real situations which dispersion a process is meant to remain constant with regard its position. To this aim, several control charts have been recently proposed literature. In paper, new one-sided adaptive monitor MCV are proposed. The chart applies variable sampling interval (VSI) strategy on an exponenti...

Multivariate statistical process control charts are often used for process monitoring to detect out-of-control anomalies. However, multivariate control charts based on conventional statistical distance measures, such as the one used in the Hotelling’s T 2 control chart, cannot scale up to large amounts of complex process data, e.g. data with a large number of variables and a high rate of data s...

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...

In some statistical process monitoring applications, quality of a process or product is described by more than one ordinal factors called ordinal multivariate process. To show the relationship between these factors, an ordinal contingency table is used and modeled with ordinal log-linear model. In this paper, a new control charts based on ordinal-normal statistic is developed to monitor the ord...

For attribute data with (very) low rates of defectives, attractive control charts can be based on the maximum of subsequent groups of r failure times, for some suitable r ≥ 1, like r = 5. Such charts combine good performance with often highly needed robustness, as they allow a nonparametric adaptation already for Phase I samples of ordinary size. In the present paper we address the problem of e...

In some multivariate statistical control applications, the data of the process cannot be precise and defined linguistically in practice. Using multivariate control charts in such situations with non-precise data leads to misleading results. In this paper, a new neural network-based monitoring scheme is presented by considering fuzzy multivariate multinomial data. The proposed approach is also a...

Monitoring several correlated quality characteristics of a process is common in modern manufacturing and service industries. Although lot attention has been paid to monitoring the multivariate mean, not many control charts are available for covariance matrix. This paper presents comprehensive overview literature on matrix statistical (MSPM) framework. It classifies research that previously appe...