Siemens Aktiengesellschaft

Maximilian Metzner

Francesco Montrone

Jörn Peschke

Thomas Baudisch

Ulrich Rossgoderer

Sören Weissert

Simon Hergenröder

Daniel Fiebag

A computer-implemented method and system provides a labelled training dataset. At least one sub-object or component is selected in a CAD model of an object comprising a plurality of sub-objects or components. A plurality of different render images is generated and the different render images contain the at least one selected sub-object or component. The different render images are labelled on the basis of the CAD model to provide a training dataset based on the labelled render images. Also, a computer-implemented method provides a trained function that is trained on the training dataset. A computer-implemented image recognition method uses such the trained function. An image recognition system comprising an image capture device and a data processing system carries out the image recognition method. A computer program comprises instructions that cause the system to carry out the methods.

Method and systems for providing synthetic labeled training datasets and applications thereof

A method, device for identifying causes of faults in automated systems and an automated system which forms the device for identifying causes of faults in automated systems, wherein within a digital twin of the automated system, at least one element of the digital twin is assumed to be faulty and then simulated using the digital twin until a fault time, and where at least one faulty element of the automated system is identified as the cause of a fault based on the at least one element assumed to be faulty.

Method and device for efficient fault analysis through simulated faults in a digital twin

A method and apparatus for determining differences between an automated installation and a digital twin of the automated installation and to an automated installation that includes the apparatus, wherein a sensor unit is fastened to a process material and passes through an automated installation together with the process material where, during this process, the sensor unit continuously records at least one measurement variable, a temporal profile of the recorded measurement variable is compared with a temporal profile of a corresponding simulated measurement variable and if there is a difference between the temporal profiles, then the location in the automated installation at which the difference is present is identified.

Apparatus and method for identifying differences between a real installation and a digital twin of the installation

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Bamberg, Germany

Maximilian Metzner