Siemens Aktiengesellschaft

Siemens Aktiengesell Schaft

Daniel Schall

Stefan Von Dosky

Thomas Bierweiler

Josef Alois Birchbauer

Michael Hornacek

Dietmar Schmidt

Paul Schneeweiss

Christoph Elsner

Stefan Trabesinger

Andre Butzerin

Roman Khazankin

Jana Kemnitz

Thomas HIEßL

Sebastian Meixner

A method for operating a terminal with high availability in a network, where a memory stores an identifier, for a backup device of a first edge device, which relates to a second edge device, stores a model for operating the terminal and stores information about the connection configuration between the first and second edge devices, the monitoring device and the terminal, where the first edge device periodically sends a signal about its correct operation, the signal is received by the monitoring device, and detection of an absent or erroneous signal results in an associated error device being detected, and the model and the connection configuration of the error device being transferred from the memory to a substitute device, the substitute device being determined via the identifier associated with the absent or erroneous signal, and the substitute device operating the terminal using the received model and the connection configuration.

Computer-implemented method for operating a terminal with high availability in a network

Apparatus and method for operating a machine with a tool, wherein the method includes capturing at least one operating data point for the machine and/or the tool, calculating at least one estimate value from the at least one operating data point based on an ML model, determining an anomaly in the form of a discrete-time anomaly value based on the at least one estimate value via a comparison with a predetermined comparison value for the at least one estimate and detection of a match for the estimate comparison, storing the discrete-time anomaly value in a memory and aggregating the trend in the anomaly value over time to form a smoothed anomaly value, comparing the smoothed anomaly value with at least one predetermined comparison value for the anomaly and detecting a match for the anomaly comparison, and outputting a control operation to the machine based on the smoothed anomaly value.

Method and apparatus for operating a machine with a tool

Method for monitoring operation of a technical object, wherein a) a first orientation of the first acceleration sensor in the form of a position vector is determined, b) the mathematical model for operation of the object is generated and trained, c) the first acceleration sensor is disassembled and a three-axis replacement acceleration sensor is assembled with a new orientation on the object, d) acceleration values are detected, e) respective indicator values are calculated from the temporal course of the detected acceleration values of the replacement acceleration sensor, f) a replacement vector is determined from the indicator values and a differential vector between the replacement vector and the position vector of the first acceleration sensor determined in step b) is determined, and g) the model during operation of the object for the position vector in the orientation of the replacement vector is applied by taking into account the differential vector.

Method and apparatus for monitoring operation of a technical object

A method and device for machine learning wherein, in order to create favorable method conditions, an at least first machine learning model is trained via an at least first data set, a second machine learning model is trained via a second data set, an at least first prediction data set is formed via the trained at least first machine learning model, a second prediction data set is formed via the trained second machine learning model, a linking machine learning model is trained at least via the first prediction data set and the second prediction data set, a third prediction data set is formed via the linking machine learning model, and controlled variables for controlling a control apparatus are formed at least via the third prediction data set, such that the demand for computing power is reduced and the prediction accuracy and control accuracy are increased.

Method and device for machine learning in a computing unit

A method for visualizing and validating process events in process monitoring systems, wherein a permanently installed sensor system reports states to a process monitoring system, if predefined limit values are exceeded, then the process monitoring system initiates, plans and performs local data acquisition using a mobile sensor, where the result of this data acquisition is analyzed and visualized in the process monitoring system and is integrated in the state information relating to the process or the installation.

System and method for visualizing and validating process events

Growing community of inventors

Vienna, Austria

Daniel Schall