Fault detection in continuous processes using multivariate statistical methods

The approach to process monitoring known as multivariate statistical proces s control (MSPC) has developed as a distinct technology, closely related to the field of fault detection and isolation. A body of technical research a nd industrial applications indicate a unique applicability to complex large -scale processes, but has paid relatively little attention to generic live process issues. In this paper, the impact of various classes of generic abn ormality in the operation of continuous process plants on MSPC monitoring i s investigated. It is shown how the effectiveness of the MSPC approach may be understood in terms of model and signal-based fault detection methods, a nd how the multivariate tools may be configured to maximize their effective ness. A brief review of MSPC for the process industries is also presented, indicating the current state of the art.