Solution Approach: 1) In a first step, the data acquisition problem is tackled. For this, data from all parts of the plant (sensors, actors, MES-systems, ERP-systems, energy consumptions) must be gathers and synchronized. As a result, plant operators are able to visualize a synchronized overall status of their plant. 2) In a second step, these data is recorded, analyzed and abstracted. This is done by means of statistics, machine learning, and data mining. At the end of this step, a model of the normal and of the faulty plant behavior over a period of time is identified. This model can already be used to predict the plant behavior and to identify optimization potentials. 3) Finally, by comparing the real plant behavior to the behavior predicted by the learned models, non-normal or suboptimal situations can be identified automatically during the plant operation. Furthermore, error causes can also be identified.
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