Siemens Aktiengesellschaft

Siemens Aktiengesell Schaft

Siemens Industry Software Nv

Harald Held

Michael Metzger

Florian Steinke

Tim Schenk

Till Heinzerling

Sanjeev Srivastava

Wendelin Feiten

Kai Wurm

Jan Christoph Wehrstedt

Andrés Botero Halblaub

Moritz Allmaras

Rafael Fink

Jan Götz

Christoph Heinrich

Annelie Sohr

Benjamin Lee

Yayun Zhou

Philipp Emanuel Stelzig

Ulli Herzog

Sascha Heß

Holger Hanss

Julian Knittel

Wei Xia

Denis Smirnov

Steffen Limmer

Guido Hülsmann

Markus Lade

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

A computer-implemented method for manufacturing or controlling a technical system includes the method steps: (a) inputting a sample of parameter sets suitable for manufacturing or controlling the technical system, with a feasibility identifier assigned to each of the parameter sets, wherein the feasibility identifier marks each parameter set either as technically feasible or technically non-feasible or erroneous in terms of manufacturing or controlling the technical system, (b) generating a computerized surrogate model for the technical system based on the respective parameter sets of the sample, which are marked as technically feasible, by means of a regression method, (c) determining an optimized parameter set based on the surrogate model by means of a computerized optimization method, and (d) outputting the optimized parameter set for manufacturing or controlling the technical system.

Manufacturing or controlling a technical system using an optimized parameter set

An aircraft that includes a helicopter drone on which a 3D scanner is mounted via an actively rotatable joint is provided. The 3D scanner has at least one high-resolution camera for recording a multiplicity of overlapping images of the object from different recording positions and recording directions, so that comparison of the images allows a position and orientation of the 3D scanner relative to the object to be ascertained. In addition, the aircraft has a coordination device for coordinated control of the 3D scanner, the joint and the helicopter drone. The system for damage analysis has an aircraft and an image processing module generating a data representation of a surface profile of the object on the basis of the recorded images. In addition, the system includes a rating device for checking the surface profile and for outputting a damage statement on the basis of the check.

Moving flying object for scanning an object, and system for analyzing damage to the object

Provided is a control device for a production module having a data memory for storing operational settings of production modules and restrictions, which must be complied with by at least some of the operational settings. A settings management module is used to determine the external operational setting of an adjacent production module on which a local operational setting of the production module is dependent on the basis of a common restriction. An optimization module is also provided and has a local assessment function, which assesses the local operational setting, and a further assessment function which assesses noncompliance with the common restriction.

Control device for a production module, production module having a control device, and method for operating the control device

The rotational speed of the drum of an ore mill may be controlled variably over time. By rotating the drum during a first time interval at high speed, especially hard or dense particles are broken up by tumbling. At the same time, the discharge characteristics of the mill are adversely affected. In a subsequent second time interval, the drum is rotated at a slower speed, and the material is discharged more effectively, whereas the tumbling movement inside the mill is not achieved. The combination of said different modes of operation within short time periods in continuous operation may improve both the comminution as a result of a tumbling motion of the material and also the discharge of the ground material. By regulating the rotational speed with different target values within short time windows, different requirements for the movement behavior of the material to be ground and for the discharge characteristics of the ground material can be simultaneously optimized. This may allow a higher throughput for the mill.

Method for operating a mill

A method is disclosed for operating a mill at continuous input and output mass flows, and to the mill, wherein a process model based on power balance equations and mass balance equations is used. Characteristic process variables can be measured in a simple manner outside the mill. Characteristic process variables which are still not known can be ascertained by means of inserting the measured values into a respective power balance equation, assuming that the other process variables are in each case known or are insignificant. On this basis, the mill can be actuated in an optimum manner in order to provide a high output power.

Use of temperature measurements for indirect measurement of process variables in milling systems

An assembly is provided for receiving characteristic data of a mill including a rotating mill sleeve having rotor coils and a stator having stator coils, wherein oscillations of the mill sleeve are transmitted to stator coils and/or measurement coils on the stator. The assembly may include stator coil(s) configured to tap power supply induction voltages to detect oscillations of the mill sleeve, wherein the stator coil(s) and/or measurement coil(s) designed to tap induction voltages are positioned in a sector of the mill sleeve in which large oscillations are expected based on the scale of the mill sleeve. Further, a method includes determining the induction voltage induced on stator coil(s) and/or measurement coil(s) by tapping at least one stator coil power supply and/or by tapping at least one measurement coil, and deriving status variable(s) of a milling method that reflect the fill level status in the sector of the respective coil.

Assembly, operating method and circuit for a mill driven by a ring motor

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Haar, Germany

Harald Held