Patents by Inventor Xiahui Hu

Xiahui Hu 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).

  • Apparatus and method for screening data for kernel regression model building
    Patent number: 10733533
    Abstract: Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.
    Type: Grant
    Filed: March 7, 2017
    Date of Patent: August 4, 2020
    Assignee: General Electric Company
    Inventors: Shaddy Abado, Jianbo Yang, Xiahui Hu, Abhinav Saxena, Charmin Patel
  • APPARATUS AND METHOD FOR SCREENING DATA FOR KERNEL REGRESSION MODEL BUILDING
    Publication number: 20180260733
    Abstract: Raw data is received from an industrial machine. The industrial machine includes one or more sensors that obtain the data, and the sensors transmit the raw data to a central processing center. The raw data is received at the central processing center and an unsupervised kernel-based algorithm is recursively applied to the raw data. The application of the unsupervised kernel-based algorithm is effective to learn characteristics of the raw data and to determine from the raw data a class of acceptable data. The class of acceptable data is data having a degree of confidence above a predetermined level that the data was obtained during a healthy operation of the machine. The acceptable data is successively determined and refined upon each application of the unsupervised kernel-based algorithm. The unsupervised kernel-based algorithm is executed until a condition is met.
    Type: Application
    Filed: March 7, 2017
    Publication date: September 13, 2018
    Inventors: Shaddy Abado, Jianbo Yang, Xiahui Hu, Abhinav Saxena, Charmin Patel