Fault detection in continuous processes using multivariate statistical methods

The approach to process monitoring known as multivariate statistical process control (MSPC) has developed as a distinct technology, closely related to the field of fault detection and isolation. A body of technical research and 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 abnormality in the operation of continuous process plants on MSPC monitoring is investigated. It is shown how the effectiveness of the MSPC approach may be understood in terms of model and signal-based fault detection methods, and how the multivariate tools may be configured to maximize their effectiveness. A brief review of MSPC for the process industries is also presented, indicating the current state of the art.