Siemens Aktiengesellschaft

Siemens Industry Software Nv

Technische Universitát Dresden

Tim Schenk

Dragan Obradovic

Ingo Thon

Till Heinzerling

Andrés Botero Halblaub

Jan Christoph Wehrstedt

Moritz Allmaras

Andreas Pirsing

Harald Held

Heiko Patzlaff

Annelie Sohr

Mathias Maurmaier

Jan Fischer

Andreas Stutz

Leon Urbas

Roland Rosen

Steffen Limmer

Christoph Wincheringer

Veronica Brandstetter

Anselm Klose

Jonathan Maedler

Stephan Hensel

Markus Graube

Julian Knittel

Sascha Heß

Ulli Herzog

Mathias Oppelt

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 a device for configuring a controller and to a method and a controller for controlling a technical system by means of a data-based control model is provided, in particular a model based on reinforcement learning. This data-based control model is configured using a model-predictive control model. Configuration parameters of the data-based control model are set by mapping the model-predictive control model onto the data-based control model in such a way that the data-based control model reproduces the output data of the model predictive control model depending on state data of the technical system read in, and determines optimized control parameters configured in this way. A computationally intensive training procedure for configuring the data-based control model can thus be avoided.

Controlling a technical system by data-based control model

To automate alarm management in modular production installations, provided is, with respect to a modular production installation, for which, for its modular, manufacturer-independent construction and operation, 'process equipment assembly' modules, controlled by a programmable PEA controller and simulated by means of a configurable PEA simulation, are integrated into the production installation, by means of a “module type package <MTP>” mechanism for the PEA module description, in the course of a process orchestration which is standardized the following is carried out in the alarm management: —all states of the modular production installation with respect to triggered faults of possible fault cases of the production installation are virtually run through and simulated; —simulation faults that occur are logged concomitantly by means of alarms, which provide identification and an alarm message log is created and/or alarm chains are identified and alarm relationships in the alarm chains are determined.

Method and system for managing alarms in modular production installations

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

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

Provided is a method for operating a system including components controllable by control variables, including the determining of at least one optimized control variable at an optimization period for minimizing or maximizing a target function of the system in such a way that at least one system variable fulfills predetermined secondary conditions with respect to the control period, wherein the determining includes the establishment of an evaluation function for each of the secondary conditions to evaluate a change of the system variable at the end of the optimization period based on a current value of the system variable at the beginning of the optimization period, based on a gradient of the system variable relating to the secondary condition and maximally achievable over the control period, and based on a gradient relating to the secondary condition and minimally achievable over the control period.

Method and apparatus for operating a technical system

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Landshut, Germany

Tim Schenk