Statistical process control plays a key role in today's highly competitive industrial
environment since it allows quality practitioners to timely detect out‐of‐control situations and …

Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …

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 quality variables or data streams remains an important and
challenging problem in statistical process control (SPC). Although the multivariate SPC 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 …

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

Abstract “Big data” is a buzzword these days due to an enormous amount of data-rich
applications in different industries and research projects. In practice, big data often take the …

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

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 …