In some applications of statistical process monitoring, a quality characteristic can be characterized by linear regression relationships between several response variables and one explanatory variable, which is referred to as a “multivariate simple linear profile.” It is usually assumed that the process parameters are known in Phase II. However, in most applications, this assumption is viola...

A control chart is one of the statistical process techniques that used to monitor different processes. Some processes are characterized by functions or profiles, and a profile functional relationship between dependent independent variable(s) quality process. Several research studies were conducted on linear profiling where only fixed effects considered. However, in this research, we focus rando...

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient's vital signs in the m...

In recent years, smart phones with inbuilt sensors have become popular devices to facili13 tate activity recognition. The sensors capture a large amount of data, containing meaningful events, 14 in a short period of time. The change points in this data are used to specify transitions to distinct 15 events and can be used in various scenarios such as identifying change in a patient’s vital signs...

The general assumption for designing a multivariate control chart is that the multiple variables are independent and normally distributed. This may not be tenable in many practical situations, because with dependency often need to monitored simultaneously ensure process in-control. Gumbel’s Bivariate Exponential (GBE) distribution considered better model skewed data reliability analysis. In thi...

One of the most powerful tools in quality control is the statistical control chart. First developed in the 1920's by Walter Shewhart, the control chart found widespread use during World War II and has been employed, with various modifications ever since. The drawbacks to multivariate charting schemes is their inability to identify which variable was the source of the signal. The multivariate ex...

Advances in additive manufacturing (AM) processes have increased the number of relevant applications various industries. To keep up with this development, process stability AM should be monitored, which is conducted through assessment outputs or product characteristics. However, use univariate control charts to monitor an might lead misleading results, as most additively manufactured products m...