A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata
This addresses the challenge of automating anomaly detection for hybrid systems, reducing manual modeling efforts and costs, though it appears incremental as it builds on existing deep learning and timed automata techniques.
The paper tackles anomaly detection in hybrid production systems by introducing DAD:DeepAnomalyDetection, which combines deep learning and timed automata to automatically learn behavioral models from observations, showing promising results on real-world datasets.
Performing anomaly detection in hybrid systems is a challenging task since it requires analysis of timing behavior and mutual dependencies of both discrete and continuous signals. Typically, it requires modeling system behavior, which is often accomplished manually by human engineers. Using machine learning for creating a behavioral model from observations has advantages, such as lower development costs and fewer requirements for specific knowledge about the system. The paper presents DAD:DeepAnomalyDetection, a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The ability of deep belief nets to extract binary features from real-valued inputs is used for transformation of continuous to discrete signals. These signals, together with the original discrete signals are than handled in an identical way. Anomaly detection is performed by the comparison of actual and predicted system behavior. The algorithm has been applied to few data sets including two from real systems and has shown promising results.