Patents by Inventor Cecilia Margareta Bruhn

Cecilia Margareta Bruhn has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20250053143
    Abstract: Various embodiments of the teachings herein include a method for controlling a battery production system. An example includes: measuring production parameters with a plurality of sensors; determining a quality value of a battery cell with the measurement values; calculating a dependency of the quality value on the measurement values with a computing unit; calculating a dependency of the quality value on changed production parameters by performing a machine learning method; determining a controllable process parameter with an improved quality value using a parameter optimization for the machine learning method including a Bayesian optimization, wherein measurement values with associated quality values are included in the optimization as reference points; and using the controllable process parameter with an improved quality value in operation of the battery production system.
    Type: Application
    Filed: October 28, 2022
    Publication date: February 13, 2025
    Applicant: Siemens Aktiengesellschaft
    Inventors: Axel Reitinger, Manfred Baldauf, Sascha Schulte, Jonas Witt, Cecilia Margareta Bruhn, Barbara Schricker, Clemens Otte
  • Publication number: 20240296383
    Abstract: Computer-implemented method for generating a combined machine learning model is provided, including the steps: a. providing a trained unsupervised machine learning model for anomaly detection; determining at least one output label for each data item of a plurality of data items of unlabeled application data; c. transmitting the at least one determined output label to a user; d. receiving at least one processed output label, at least one additional data item or at least one additional output label from the; e. training at least one additional machine learning model for anomaly detection; f. generating the combined machine learning model for anomaly detection using a connection function based on the trained unsupervised machine learning model and the at least one trained additional machine learning model; and g. providing the combined machine learning model as output.
    Type: Application
    Filed: July 7, 2022
    Publication date: September 5, 2024
    Inventors: Cecilia Margareta Bruhn, Andreas Hangauer, Michael Lebacher
  • Publication number: 20240118685
    Abstract: Assistance apparatus for automatically identifying failure types of a technical system is provided including at least one processor configured to determine for each sensor data a set of specific temporal courses of first time series of the sensor data of the sensor and assign a symbolic representation to each of the different specific temporal courses, provide at least one failure pattern, obtain more than one monitored time series of sensor data of the technical system, each of them divided into a sequence of time segments, and automatically assign to each time segment a symbolic representations according to the temporal course of the sensor data in the time segment, calculate a similarity measure for the set of symbolic representations of a selected time interval, determine a ranking of the failure pattern depending on decreasing values of the calculated similarity measure, and output the ranking.
    Type: Application
    Filed: November 17, 2021
    Publication date: April 11, 2024
    Applicant: Siemens Aktiengesellschaft
    Inventors: Stefan Hagen Weber, Johannes Kehrer, Johanna Bronner, Cecilia Margareta Bruhn, Michael Schnurbusch
  • Publication number: 20230375441
    Abstract: A monitoring device including an analysing unit configured to obtain an actual sensor data point, determine whether the actual sensor data point is an outlier, determine whether the actual data point represents a discontinuity, determine a slope by a regression model of a slope equation of a straight line in time fitted to at least a predefined first number of subsequently obtained sensor data points, and determine whether the actual sensor data point belongs to the learned regression model, if the actual sensor data point does not belong to the learned regression model, determine a new slope based on the actual data point and a predefined second number of preceding sensor data points, and create a segment including all sensor data points, and display each sensor data point indicating the determined segment or being an outlier.
    Type: Application
    Filed: September 28, 2021
    Publication date: November 23, 2023
    Inventors: Cecilia Margareta Bruhn, Michael Schnurbusch, Michael Lebacher, Johanna Bronner