Over the past decades, control charts, one of the essential tools in Statistical Process Control
(SPC), have been widely implemented in manufacturing industries as an effective approach …

Industrial control charts are used in manufacturing to quickly and robustly indicate the status
of production and to prompt any necessary corrective actions. The library of tools available …

The most prevalent use of multivariate statistical process monitoring (SPM) is in the
production industries. In SPM, data are usually assumed to be uncorrelated in both …

In most of the process monitoring, it is assumed that the observations from the process
output are independent and identically distributed. But for many processes, the observations …

Detecting early warning signals of developing mood disorders in continuously collected
affective experience sampling (ESM) data would pave the way for timely intervention and …

Two types of control charts exist based on different quality characteristics: variable and
attribute. These characteristics are commonly monitored using separate procedures. Only a …

On-line monitoring of process variability is strategic to achieve high standards of quality and
maintain at acceptable levels the number of nonconforming items. Shewhart control charts …

In practice most processes are known to produce autocorrelated observations.
Autocorrelation degrades the performance of control charts by producing frequent false …

The usual key assumptions in designing quality control charts are the normality and
independency of serial samples. While the normality assumption holds in most cases, in …

Multivariate auto‐correlated process control issues in industrial systems are a concern for
statistical process monitoring (SPM). Traditional control charts produce large false alarms …