Anomaly Detection and Mode Identification in Multimode Processes Using the Field Kalman Filter

Abstract

A process plant can have multiple modes of operation due to varying demand, availability of resources, or the fundamental design of a process. Each of these modes is considered as normal operation. Anomalies in the process are characterized as deviations away from normal operation. Such anomalies can be indicative of developing faults which, if left unresolved, can lead to failures and unplanned downtime. The field Kalman filter (FKF) is a model-based approach, which is adopted in this article for monitoring a multimode process. Previously, the FKF has been applied in process monitoring to differentiate normal operation from known faulty modes of operation. This article extends the FKF so that it may detect occurrences of anomalies and differentiate them from the various normal modes of operation. A method is proposed for off-line training an FKF monitoring model and online monitoring. The off-line part comprises training an FKF model based on multivariate autoregressive state-space (MARSS) models fitted to historical process data. A monitoring indicator is also introduced. Online monitoring, based on the FKF for anomaly detection and mode identification, is demonstrated using a simulated multimode process. The performance of the proposed method is also demonstrated using data obtained from a pilot-scale multiphase flow facility. The results show that the method can be applied successfully for anomaly detection and mode identification.