  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.
