Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. A learning anomaly detection algorithm for hybrid manufacturing systems
 Feldman, A.: DX 2013, 24th International Workshop on Principles of Diagnosis. Proceedings : 1.4. Oktober 2013, Jerusalem Jerusalem, 2013 S.4552 
 International Workshop on Principles of Diagnosis (DX) <24, 2013, Jerusalem> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IOSB () 
Abstract
For complex and distributed technical systems, modelbased anomaly detection solutions often show better results than approaches, which do not use explicit behavior models. But sofar, the creation and maintenance of such models turned out to be the major problem of such modelbased approaches. One solution to this problem is provided by machine learning: Behavior models can be learned automatically based on system observations. Such a machinelearningbased 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.