Woodall and Montgomery [35] in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality process characteristics is necessary. Process monitoring problems in which several related variabl...

In order to better utilize historical process data from faulty operations, supervised learning methods, such as Fisher discriminant analysis (FDA), have been adopted in process monitoring. However, such methods can only separate known faults from normal operations, and they have no means to deal with unknown faults. In addition, most of these methods are not designed for handling non-Gaussian d...

Statistics pattern analysis (SPA) is a new multivariate statistical monitoring framework proposed by the authors recently. It addresses some challenges that cannot be readily addressed by the commonly used multivariate statistical methods such as principal component analysis (PCA) in monitoring batch processes in the semiconductor industry. It was later extended to the monitoring of continuous ...

Statistical Process Control (SPC) and its variations have shown to be a powerful technique to predict unstable and incapable processes. However, SPC lacks the ability to quantify the necessary changes to correct deviations in the process when incapability is detected. On the other hand, a technique based on control theory has shown to be adequate to quantify changes in the test process in order...