Using Siamese Neural Networks for the Open Set Recognition of Anomalies Detected in Industrial Time Series Data
Marcel Dix, Jan Jens Koltermann, Sebastian Mieck, Heiko Petersen, Sebastian Taege, Gahana Anjanappa · 2024
Being able to pinpoint the type of anomaly goes beyond basic anomaly detection, which simply flags unusual events. It empowers users to gain a deeper understanding of the problem. However, most recognition/classification algorithms struggle with unknown data (Open Set Recognition (OSR) problem), which poses a critical limitation for industrial applications where unforeseen faults can occur. This paper investigates Siamese Neural Networks (SNNs) for recognizing anomalies in industrial time series data. Our use case involves developing an anomaly detection system for power plant operators. We evaluate the effectiveness of SNN s using real data from a power plant in Germany, and two additional public datasets (MaFaulDa and TEP), demonstrating SNN s as a transferable solution for overcoming the OSR problem in the industrial domain.