Evaluation of Performance of Anomaly Detection Systems Based on Adaptive Resonance Theory

Abstract

Early detection of anomalies is crucial for maintaining high productivity in industrial plants. To meet that requirement, anomaly detection systems based on adaptive resonance theory (ART) have been developed. Although various anomaly detection methods based on ART have been proposed, their performances have not yet been evaluated systematically. A new anomaly detection criterion is proposed, and the performances of ART-based anomaly detection systems, using different anomaly detection criteria and different anomaly detection structures, are evaluated. The performance evaluation results show that distributed model based systems, which use an ART model for each part of the plant, attain higher anomaly detection performance than that of systems using an ART model for the entire plant. They also show that an anomaly detection system using a new anomaly detection criterion, based on the distance between samples, attains almost the same anomaly detection performance as that of a system using generation of new categories.