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 …

High-dimensional data refers to a dataset that contains many variables or features, typically
with many more features p than observations n (ie n< p). With technological advancements …

Statistical process control (SPC) charts play a central role in quality control and
management. Many conventional SPC charts are designed under the assumption that the …

In applications, most processes for quality control and management are multivariate. Thus,
multivariate statistical process control (MSPC) is an important research problem and has …

Monitoring multivariate and high-dimensional data streams is often an essential requirement
for quality management in manufacturing and service sectors in the Industry 4.0 era …

Abstract Machine learning methods have been widely used in different applications,
including process control and monitoring. For handling statistical process control (SPC) …

Improvements in measuring devices, the development of sensing technologies, automated
record-keeping and cloud storage facilities have offered us a large volume of data streams …

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

In this paper, we develop two adaptive approaches for detecting the signal source in a
bivariate process when a shift occurs in the location vector or the scale matrix or both. The …

Monitoring time-between-events-and-amplitude (TBEA) data, including the time interval
between two successive nonconforming events and the amplitude of an event, is significant …