Technological advances led to increasingly larger industrial quality-related datasets calling
for process monitoring methods able to handle them. In such context, the application of …

Process monitoring of multivariate quality attributes is important in many industrial
applications, in which rich historical data are often available thanks to modern sensing …

In the context of distribution-free process monitoring and control, the Cucconi statistic has
attracted more and more attention in recent years. The original Cucconi statistic was …

In the context of big data-driven smart manufacturing, data is often characterized by high
dimensionality, numerous variables, and complex associations. As a result, mixed …

Monitoring high-dimensional processes is a challenging task, as the underlying dependency
structure among variables is often too complicated to estimate accurately. The inherent …

Conventional statistical process control tools monitor either continuous or count data but
rarely both simultaneously. While process data are becoming increasingly complex, there …

Monitoring complex systems involving high-dimensional data streams (HDS) provides quick
real-time detection of abnormal changes of system performance, but accurate and efficient …

An adaptive control chart is one of the most effective techniques in Statistical Process
Control (SPC). The coefficient of variation (CV) is common in many real life applications …

This paper offers new multivariate statistical process monitoring schemes to study the
process shift supported by a distinctive product quality features assessment. The proposed …

Parametric and nonparametric multivariate control charts that are proven very useful in
monitoring the covariance matrix of multivariate normally or “nearly” normally distributed …