Real-time risk assessment and surveillance for early prediction of unplanned shutdown events

Abstract

Statistical Process Monitoring (SPM) and Fault Detection & Diagnosis (FDD) are reactive methodologies by design. However, in some situations, the prompt detection ex post facto is totally unsatisfactory. This is the case of Unplanned Shutdown Events (USE), i.e., major disruptions that cause the process to be shutdown. In this article, a new proactive approach is proposed that monitors the risk of USE occurring in the near future, called Risk Assessment and Surveillance (RAS). Contrary to FDD/SPM, RAS is a risk-based approach that continuously predicts the probability of a major disruption taking place in the near future horizon. The predictive model is based only on high-resolution process data and its outcomes are classified in three zones: normal, warning (where countermeasures can prevent the USE), and critical (possibly irreversibly leading to shutdown). It is shown that, in some cases, the method can alert the operators several hours before they actually took any corrective actions. A process improvement diagnosis methodology is also presented for identifying conditions promoting USE’s, based on coarse-grained modeling of the Remaining Useful Life (RUL).