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In self-organizing industrial systems (SOIS) workflows are not defined by engineers in advance, but the system decides by itself at runtime how to route workpieces through the factory, so that the desired output is manufactured as optimal as possible in the present circumstances. As a consequence, the number of possible workflows is not limited to those which were manually predefined, but limited...
The main challenge for anomaly detection in Self-Organizing Industrial Systems (SOIS) is the high degree of freedom of the system, which causes a state-space explosion. Since the system is free to choose at runtime any solution out of the vast amount of possible ones, to ensure that the production process is optimal at all times, classic anomaly detection techniques can not be used one-to-one in SOISs...
In this work, we present a monitoring system for Self-Organizing Industrial Systems (SOIS). It is based on an anomaly detection approach which evaluates the movement of objects within a factory by putting them together from sub-trajectories. By introducing two metrics — relative user frequency and pathlet occurence per user — the existing method is extended so that not only anomalous trajectories...
The great success of wearables and smartphone apps for provision of extensive physical workout instructions boosts a whole industry dealing with consumer oriented sensors and sports equipment. But with these opportunities there are also new challenges emerging. The unregulated distribution of instructions about ambitious exercises enables unexperienced users to undertake demanding workouts without...
In this paper, we present a novel anomaly detection method which addresses the main challenge of self-organizing industrial systems: the state space explosion. In particular, the flexibility and dynamic nature of such systems result in an exponentially growing number of possible execution plans. To handle this problem, we propose to learn the underlying topology, instead of storing whole paths a work-piece...
In order to accurately predict future states of a smart cyber-physical system, which can change its behavior to a large degree in response to environmental influences, the existence of precise models of the system and its surroundings is demandable. In machine engineering, ultra-high fidelity simulations have been developed to better understand both constraints in system design and possible consequences...
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