In this paper we discuss the basic procedures for the implementation of multivariate
statistical process control via control charting. Furthermore, we review multivariate …

Control charts are powerful tools used to monitor the quality of processes. In practice, control
chart limits are often calculated using parameter estimates from an in-control Phase I …

In most statistical process control (SPC) applications, it is assumed that the quality of a
process or product can be adequately represented by the distribution of a univariate quality …

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 …

The aim of this book is to summarize major achievements in statistical profile monitoring
methods. Statistical profile monitoring can be considered as a potential subarea of statistical …

We study the phase I analysis of data when the quality of a process or product is
characterized by a linear function. We assume that simple linear regression data are …

In many quality control applications, use of a single (or several distinct) quality characteristic
(s) is insufficient to characterize the quality of a produced item. In an increasing number of …

Three different operational curves-the power curve, rotor curve, and blade pitch curve-are
presented for monitoring a wind farm's performance. A five-year historical data set has been …

Profile monitoring is a relatively new set of techniques in quality control used when the
product or process quality is best represented by a function (or a curve) at each time period …

Multivariate control charts, such as Hotelling's T 2 control chart, are used for monitoring
several quality characteristics measured simultaneously on a product or process. In the …