Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …

Control charts that are based on assumption (s) of a specific form for the underlying process
distribution are referred to as parametric control charts. There are many applications where …

The purpose of this paper is to provide an overview and our perspective of recent research
and applications of statistical process monitoring. The focus is on work done over the past …

Condition monitoring of the wind turbine based on supervisory control and data acquisition
(SCADA) data has attracted much attention in recent years. Nevertheless, there are some …

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 …

In some applications, the quality of a process is characterized by the functional relationship
between a response variable and one or more explanatory variables. Profile monitoring is …

High-dimensional data monitoring and diagnosis has recently attracted increasing attention
among researchers as well as practitioners. However, existing process monitoring methods …

Nonparametric control charts are useful in statistical process control (SPC) when there is a
lack of or limited knowledge about the underlying process distribution, especially when the …

Process monitoring and fault diagnosis using profile data remains an important and
challenging problem in statistical process control (SPC). Although the analysis of profile data …

The increasing availability of high-volume, high-velocity data sets, often containing variables
of different data types, brings an increasing need for monitoring tools that are designed to …