Siemens Aktiengesellschaft

Moritz Allmaras

Jan Christoph Wehrstedt

Utz Wever

Tim Schenk

Birgit Obst

Till Heinzerling

Annelie Sohr

Andreas Pirsing

Roland Rosen

Mathias Oppelt

Ulli Herzog

Sascha Heß

Julian Knittel

Steffen Limmer

A training data generator includes an interface to read symbols extracted from digital system plans for technical systems, wherein each of the system plans represents the design and/or the functionality of a technical system by of symbols, and the system plans are identical. The training data generator also includes a storage module which is designed to store the extracted symbols, a selection module which is designed to randomly select a symbol sub-quantity of the stored symbols using a random generator, a generator which is designed to generate at least one synthetic system plan on the basis of the selected symbol sub-quantity, and an output module to output the at least one synthetic system plan as training data in order to train a trainable image detection module, wherein the trainable image detection module is designed to generate a digital system plan using an analog system plan of a technical system.

Training data generator and method for generation of training datasets

Methods, devices, and assemblies for controlling pressure in a supply grid are provided. The supply grid is suitable for supplying fluid to loads. The supply grid has first sensors for measuring the flow and/or the pressure of the fluid at first locations in the supply grid and a pump for pumping the fluid or a valve for controlling the flow of the fluid. The method includes: measuring the flow and/or pressure of the fluid at the first locations in the supply grid by the first sensors; predicting the pressure at the second location in the supply grid using a self-learning system based on the measured flows or pressures, wherein the self-learning system is trained to predict the pressure at a specified location in the supply grid; and actuating the pump or the valve at least also based on the pressure predicted by the trained system at the second location.

Pressure control in a supply grid

A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.

System state prediction

To control a technical system having multiple system components, multiple functional modules access a common data model. The functional modules in this case include an interface module, a simulation module and an output module. The data model includes data components associated with the system components and simulation model data for a simulation model of the technical system. The functional modules are actuated by a flow controller, wherein the flow controller and a respective functional module have functional-module-specific selection data transmitted between them that the respective functional module takes as a basis for accessing model data of the data model. The interface module continually captures operating data of the technical system and stores them in the data model by means of selection-data-specific access.

Method and arrangement for controlling a technical system having multiple functionally linked system components

A supply network for supplying consumers with a medium is provided. Based on a reduction of data and a subsequent reconstruction of at least one pressure and/or flow value, the state at least at one further location in the supply network can be determined without having to be measured. An improved actuation of the at least one pump and/or of the at least one valve is thus possible. A device for pressure control in a supply network is also provided.

Method for pressure control in a supply network, device and supply network

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Munich, Germany

Moritz Allmaras