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A learning anomaly detection algorithm for hybrid manufacturing systems

 
: Niggemann, Oliver; Vodencarevic, Asmir; Maier, Alexander; Windmann, Stefan; Kleine Büning, Hans

Feldman, A.:
DX 2013, 24th International Workshop on Principles of Diagnosis. Proceedings : 1.-4. Oktober 2013, Jerusalem
Jerusalem, 2013
S.45-52
International Workshop on Principles of Diagnosis (DX) <24, 2013, Jerusalem>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()

Abstract
For complex and distributed technical systems, model-based anomaly detection solutions often show better results than approaches, which do not use explicit behavior models. But so-far, the creation and maintenance of such models turned out to be the major problem of such model-based approaches. One solution to this problem is provided by machine learning: Behavior models can be learned automatically based on system observations. Such a machine-learning-based solution would create an anomaly detection algorithm, which could learn and therefore could adapt itself to new situations.
In this paper, such an algorithm is described: The ANODA algorithm for anomaly detection uses the HyBUTLA algorithm to learn behavior models in form of hybrid timed probabilistic automata. For the first time, a thorough theoretical analysis of this algorithm is presented. Practical results also underline the applicability of these algorithms.

: http://publica.fraunhofer.de/dokumente/N-275734.html