Recent advances in process monitoring: Nonparametric and variable-selection methods for phase I and phase II
G Capizzi - Quality Engineering, 2015 - Taylor & Francis
The main aim of this article is to review and discuss two particular topics of statistical process
monitoring: the need for a nonparametric approach to Phase I analysis and the use of …
monitoring: the need for a nonparametric approach to Phase I analysis and the use of …
Statistical monitoring of the covariance matrix in multivariate processes: A literature review
Monitoring several correlated quality characteristics of a process is common in modern
manufacturing and service industries. Although a lot of attention has been paid to monitoring …
manufacturing and service industries. Although a lot of attention has been paid to monitoring …
Control charts for variability monitoring in high-dimensional processes
Monitoring process variability is associated with detecting changes in the covariance matrix
of a multivariate normal process. Most monitoring methods estimate the sample covariance …
of a multivariate normal process. Most monitoring methods estimate the sample covariance …
Nonparametric multivariate covariance chart for monitoring individual observations
Parametric and nonparametric multivariate control charts that are proven very useful in
monitoring the covariance matrix of multivariate normally or “nearly” normally distributed …
monitoring the covariance matrix of multivariate normally or “nearly” normally distributed …
Phase-I monitoring of high-dimensional covariance matrix using an adaptive thresholding LASSO rule
High-dimensional variability monitoring and diagnosing is of great prominence for the
quality improvement and cost reduction. Most of the existing control charts are mainly based …
quality improvement and cost reduction. Most of the existing control charts are mainly based …
A review of dispersion control charts for multivariate individual observations
JO Ajadi, Z Wang, IM Zwetsloot - Quality Engineering, 2021 - Taylor & Francis
A multivariate control chart is designed to monitor process parameters of multiple correlated
quality characteristics. Often data on multivariate processes are collected as individual …
quality characteristics. Often data on multivariate processes are collected as individual …
Simultaneous monitoring of process mean vector and covariance matrix via penalized likelihood estimation
In recent years, some authors have incorporated the penalized likelihood estimation into
designing multivariate control charts under the premise that in practice typically only a small …
designing multivariate control charts under the premise that in practice typically only a small …
Monitoring multivariate process variability with individual observations via penalised likelihood estimation
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 …
rate and poor product quality. Therefore, quick detection of changes, especially increases in …
A new multivariate EWMA scheme for monitoring covariance matrices
To monitor covariance matrices, most of the existing control charts are based on some
omnibus test and thus usually are not powerful when one is interested in detecting shifts that …
omnibus test and thus usually are not powerful when one is interested in detecting shifts that …
A new multivariate CUSUM chart for monitoring of covariance matrix with individual observations under estimated parameter
Multivariate charts for process dispersion detect changes in the variance‐covariance matrix
of a process. Most of the existing multivariate charts for monitoring the dispersion of …
of a process. Most of the existing multivariate charts for monitoring the dispersion of …