Siemens Aktiengesellschaft

Johannes Kehrer

Christoph Paulitsch

Stefan Hagen Weber

Markus Michael Geipel

Axel Reitinger

Thomas Runkler

Dieter Bogdoll

Jens Winter

Thomas Vogel

Sebastian Krueger

Ferdinand Strixner

Mahmut Halil Süer

Hiba Arnout

Johanna Bronner

Sebastian KRÜGER

An evaluation framework for a generated dataset of a data generation algorithm such as a generative adversarial network is provided. The generated dataset includes a plurality of iterations of multiple instances of generated time series of data points. The evaluation framework provides multiple views. A first view includes at least one distance measure. The at least one distance measure is between the multiple instances of the generated time series and multiple instances of a reference time series, as a function of the plurality of iterations.

Evaluation framework for time series data

A method for parameterizing an anomaly detection method, which takes a multiplicity of sensor data points as a basis for performing a density-based cluster method, including a) mapping each sensor data point in a data space into a pixel data point in a pixel space, b) reproducing at least one operation of the density-based cluster method in the data space by means of at least one pixel operation in the pixel space, c) receiving at least one parameter value for each parameter of the density-based cluster method, d) applying the at least one pixel operation in accordance with the parameter values to the pixel data points e) outputting a cluster result in visual form in the pixel space, and f) providing the received parameter values for the anomaly detection method, and an assistance apparatus for parameterizing an anomaly detection apparatus that performs the anomaly detection method.

Method and assistance system for parameterizing an anomaly detection method

A computer implemented method for automatically generating a behavior tree program for controlling a machine includes the steps of: transmitting a sequence of machine commands input by a user from a user interface to a controller, receiving supervision data in the user interface from the controller while the machine commands are executed in the controller controlling the machine, observing and copying the machine commands and supervision data transmitted between the controller and the user interface, storing the machine commands and the supervision data in a logging unit, generating a behavior tree program derived from the stored machine commands and the supervision data by statistical inference, and sending the generated behavior tree program to the controller unit to control the machine.

Method for automatically generating a behavior tree program for controlling a machine

A method for the computer-aided processing of operating data relating to a technical system, wherein the operating data have been captured during operation of the technical system in a predefined operating interval and are stored as digital data in a memory is provided. A pair of first state variables and one or more second state variables with corresponding state values of the technical system at the particular operating time are extracted from the operating data by the method. A two-dimensional representation is generated on a display of a user interface by these state variables. Bar charts for different state ranges of the pairs of first state variables are generated in this representation on the basis of the evaluation of a threshold value criterion, wherein the bar charts represent, inter alia, how many states of the technical system within the particular state range can be classified as critical.

Method for the computer-aided processing of operating data relating to a technical system

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Munich, Germany

Johannes Kehrer