Siemens Aktiengesellschaft

Technische Universitát Dresden

Jan Christoph Wehrstedt

Moritz Allmaras

Utz Wever

Tim Schenk

Andrés Botero Halblaub

Till Heinzerling

Hermann Georg Mayer

Kurt Majewski

Vincent Malik

Jan Fischer

Leon Urbas

Ulli Herzog

Sascha Heß

Jörg Nieveler

Julian Knittel

Holger Hanss

Markus Graube

Stephan Hensel

Jonathan Maedler

Anselm Klose

Veronica Brandstetter

Nils Weinert

Steffen Limmer

Veronika Brandstetter

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

A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

Sensor data generation for controlling an autonomous vehicle

A component of the technical system, a component designation and a characteristic parameter designation for a characteristic parameter of relevance to the design of the components are read and a search engine is queried with same is provided. The documents found by the search engine are read and component information, e.g. product information concerning a specific component, is extracted from said documents. The extracted component information is supplied to a machine learning routine which has been trained, using a plurality of predefined training component information and training characteristic parameter values, to reproduce predefined training characteristic parameter values using predefined training component information. Output data from the machine learning routine is selected as characteristic parameter values and inserted into a planning data record. The planning data record is then output to design the technical system.

Method and system for computer-aided design of a technical system

A method and a device for the computer-assisted simulation of a modular technical system, wherein for each real module of the modular technical system, a corresponding virtual module is generated, is provided. The virtual module includes a controller for controlling a module-specific process and at least one module-specific simulation model, to which model type information is assigned, for simulating the model-specific process. The virtual module is designed to simulate the module-specific process that is controlled by the controller by means of the module-specific simulation model and/or the controller of the real module, and is provided for a computer-assisted simulation of the modular technical system as a data container. In particular, the following allows a virtual start-up of modular process systems.

Method and apparatus for computer aided simulation of a modular technical system

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 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

A method and device are provided for leakage detection and leakage location in an area of a supply network (e.g. water supply, gas supply or district heating network), wherein measurement values of sensors of the supply network are statistically analyzed for the presence of leakages using robust regression methods. The false alarm rate (type 2 error) may be reduced or minimized.

Leakage detection and leakage location in supply networks

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Munich, Germany

Jan Christoph Wehrstedt