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    SYSTEMS AND METHODS FOR DATA ESTIMATION AND FORECASTING
    3.
    发明申请
    SYSTEMS AND METHODS FOR DATA ESTIMATION AND FORECASTING 审中-公开

    公开(公告)号：US20170193381A1

    公开(公告)日：2017-07-06

    申请号：US14986211

    申请日：2015-12-31

    Applicant: General Electric Company

    Inventor： Arun Karthi Subramaniyan ,  Felipe Antonio Chegury Viana ,  Fabio Nontao de Paula ,  Natarajan Chennimalai Kumar

    IPC: G06N7/00 ,  G06F17/30

    CPC classification number: G06N7/005 ,  G06F17/18

    Abstract: A system for estimating data in large datasets for an equipment system is provided. The system includes a data estimation and forecasting (DEF) computing device. The DEF computing device arranges a dataset in a primary matrix and parses rows of the primary matrix and generates a sample matrix by selecting primary matrix rows having non-null values for each variable. The DEF computing device adds to the sample matrix rows that include non-null values for each variable except one. The DEF computing device generates normalized values for this augmented matrix, applies several techniques including probabilistic principal component analysis (PPCA) and Markov processes, and scales the augmented matrix to normalized values. The DEF computing device generates non-null values for the variable, scales the augmented matrix back to the sample matrix, and generates a forecast for the equipment system, directing a user to update logistics processes for the equipment system.

    Quality assessment feedback control loop for additive manufacturing
    6.
    发明授权
    Quality assessment feedback control loop for additive manufacturing 有权

    公开(公告)号：US11144035B2

    公开(公告)日：2021-10-12

    申请号：US16441792

    申请日：2019-06-14

    Applicant: General Electric Company

    Inventor： Vipul Kumar Gupta ,  Natarajan Chennimalai Kumar ,  Anthony J Vinciquerra, III ,  Randal T Rausch ,  Subhrajit Roychowdhury ,  Justin John Gambone, Jr.

    IPC: G05B19/4099 ,  G06N5/02

    Abstract: A method of additive manufacturing machine (AMM) build process control includes obtaining AMM machine and process parameter settings, accessing sensor data for monitored physical conditions in the AMM, calculating a difference between expected AMM physical conditions and elements of the monitored conditions, providing the machine and process parameter settings, monitored conditions, and differences to one or more material property prediction models, computing a predicted value or range for the monitored conditions, comparing the predicted value or range to a predetermined target range, based on a determination that predicted value(s) are within the predetermined range, maintaining the machine and process parameter settings, or based on a determination that one or more of the predicted value(s) is outside the predetermined range, generating commands to compensate the machine and process parameter settings, and repeating the closed feedback loop at intervals of time during the build process. A system and a non-transitory medium are also disclosed.

    Systems and methods for data estimation and forecasting
    8.
    发明授权
    Systems and methods for data estimation and forecasting 有权

    公开(公告)号：US10438126B2

    公开(公告)日：2019-10-08

    申请号：US14986211

    申请日：2015-12-31

    Applicant: General Electric Company

    Inventor： Arun Karthi Subramaniyan ,  Felipe Antonio Chegury Viana ,  Fabio Nonato de Paula ,  Natarajan Chennimalai Kumar

    IPC: G06N7/00 ,  G06F17/18

    Abstract: A system for estimating data in large datasets for an equipment system is provided. The system includes a data estimation and forecasting (DEF) computing device. The DEF computing device arranges a dataset in a primary matrix and parses rows of the primary matrix and generates a sample matrix by selecting primary matrix rows having non-null values for each variable. The DEF computing device adds to the sample matrix rows that include non-null values for each variable except one. The DEF computing device generates normalized values for this augmented matrix, applies several techniques including probabilistic principal component analysis (PPCA) and Markov processes, and scales the augmented matrix to normalized values. The DEF computing device generates non-null values for the variable, scales the augmented matrix back to the sample matrix, and generates a forecast for the equipment system, directing a user to update logistics processes for the equipment system.

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