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Performing anomaly detection in hybrid systems is a challenging task since itrequires analysis of timing behavior and mutual dependencies of both discreteand continuous signals. Typically, it requires modeling system behavior, whichis often accomplished manually by human engineers. Using machine learning forcreating a behavioral model from observations has advantages, such as lowerdevelopment costs and fewer requirements for specific knowledge about thesystem. The paper presents DAD:DeepAnomalyDetection, a new approach forautomatic model learning and anomaly detection in hybrid production systems. Itcombines deep learning and timed automata for creating behavioral model fromobservations. The ability of deep belief nets to extract binary features fromreal-valued inputs is used for transformation of continuous to discretesignals. These signals, together with the original discrete signals are thanhandled in an identical way. Anomaly detection is performed by the comparisonof actual and predicted system behavior. The algorithm has been applied to fewdata sets including two from real systems and has shown promising results.

A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata