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 …

Sometimes the quality of a process is best expressed by a relationship between response
variables and explanatory variables. Checking over the time the stability of such functional …

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 …

With the coming of intelligent manufacturing, multi-variety and small-batch production mode
has gradually become popular. Aiming at the characteristics of high dimensional information …

There are always challenges in various industrial or non-industrial processes in which the
product quality/service is described by a large number of quality characteristics. Thus …

As our information infrastructure evolves, our ability to store, extract, and analyze data is
rapidly changing. Big data is a popular term that is used to describe the large, diverse …

In recent years, effective profile monitoring for discrete response variables, such as binary,
multinomial, ordinal or Poisson variables, has increasingly attracted interest of researchers …

In Phase I analysis, data are used retrospectively for checking process stability and defining
the in-control state. Most Phase I control charts are based on the assumption of normally …

Although several works have been proposed for multi-channel profile monitoring, two
additional challenges are yet to be addressed:(i) how to model complex correlations of multi …

In multidimensional applications, it is very rare that all variables shift at the same time. A
statistical process control procedure would have superior efficiency when limited to the …