Patents by Inventor Yeshani D. Wijesekara Gamachchige

Yeshani D. Wijesekara Gamachchige 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).

  • Predictive maintenance system using avionics ethernet networks
    Patent number: 11465779
    Abstract: A predictive maintenance system is disclosed. The system includes a network of analog and digital sensors, each sensor configured for measuring telemetry data associated with temperature levels, voltage levels, current levels, and other analog or digital parameters. The system includes microprocessors for receiving the (digitized) analog and digital telemetry data, tabulating and timestamping the raw telemetry datasets. The microprocessors compress the raw data and reduce its dimensionality by generating principal component sets from the raw data based on scalar parameters corresponding to machine learning algorithms stored to memory, the principal component sets capturing a majority of variances within the raw data. The principal component sets are organized into data packets including identifiers for the relevant algorithms. The data packets are transmitted via real time networks for either onboard storage or ground-based analysis.
    Type: Grant
    Filed: August 14, 2019
    Date of Patent: October 11, 2022
    Assignee: Rockwell Collins, Inc.
    Inventors: Daniel J. Kaplan, Christopher A. Hohensee, Yeshani D. Wijesekara Gamachchige, Chadwick K. J. Harvey
  • Predictive Maintenance System Using Avionics Ethernet Networks
    Publication number: 20210047056
    Abstract: A predictive maintenance system is disclosed. The system includes a network of analog and digital sensors, each sensor configured for measuring telemetry data associated with temperature levels, voltage levels, current levels, and other analog or digital parameters. The system includes microprocessors for receiving the (digitized) analog and digital telemetry data, tabulating and timestamping the raw telemetry datasets. The microprocessors compress the raw data and reduce its dimensionality by generating principal component sets from the raw data based on scalar parameters corresponding to machine learning algorithms stored to memory, the principal component sets capturing a majority of variances within the raw data. The principal component sets are organized into data packets including identifiers for the relevant algorithms. The data packets are transmitted via real time networks for either onboard storage or ground-based analysis.
    Type: Application
    Filed: August 14, 2019
    Publication date: February 18, 2021
    Inventors: Daniel J. Kaplan, Christopher A. Hohensee, Yeshani D. Wijesekara Gamachchige, Chadwick K.J. Harvey