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    Deep causality learning for event diagnosis on industrial time-series data
    1.
    发明授权
    Deep causality learning for event diagnosis on industrial time-series data 有权

    公开(公告)号：US11415975B2

    公开(公告)日：2022-08-16

    申请号：US16564283

    申请日：2019-09-09

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Hao Huang ,  Feng Xue ,  Weizhong Yan

    IPC: G05B23/00 ,  G05B23/02 ,  G06F17/16 ,  G06N3/08 ,  G06N3/04 ,  G05B19/406

    Abstract: According to embodiments, a system, method and non-transitory computer-readable medium are provided to receive time series data associated with one or more sensors values of a piece of machinery at a first time period, perform a non-linear transformation on the time-series data to produce one or more nonlinear temporal embedding outputs, and projecting each of the nonlinear temporal embedding outputs to a different dimension space to identify at least one causal relationship in the nonlinear temporal embedding outputs. The nonlinear embeddings are further projected to the original dimension space to produce one or more causality learning outputs. Nonlinear dimensional reduction is performed on the one or more causality learning outputs to produce reduced dimension causality learning outputs. The learning outputs are mapped to one or more predicted outputs which include a prediction of one or more of the sensor values at a second time period.

    Electric machine
    6.
    发明授权
    Electric machine 有权

    公开(公告)号：US11025104B2

    公开(公告)日：2021-06-01

    申请号：US15892962

    申请日：2018-02-09

    Applicant: General Electric Company

    Inventor： Nicholas Taylor Moore ,  Daniel Alan Niergarth ,  Paul Robert Gemin ,  Arthur Vorwerk Radun ,  Hao Huang

    IPC: H02K1/16 ,  H02K1/27 ,  H02K21/24 ,  H02K15/02

    Abstract: An electric machine includes a stator assembly including a first stator segment and a second stator segment, the first and second stator segments each including a plurality of laminations extending generally along a circumferential direction, each pair of adjacent laminations of the first and second stator segments defining a gap therebetween. The first and second stator segments are assembled together such that the laminations of the first stator segment are arranged at least partially in the gaps between the laminations of the second stator segment.

    WEAKLY-SUPERVISED EVENT GROUPING ON INDUSTRIAL TIME-SERIES DATA
    7.
    发明申请
    WEAKLY-SUPERVISED EVENT GROUPING ON INDUSTRIAL TIME-SERIES DATA 有权

    公开(公告)号：US20210092000A1

    公开(公告)日：2021-03-25

    申请号：US16578624

    申请日：2019-09-23

    Applicant: GENERAL ELECTRIC COMPANY

    Inventor： Hao Huang

    IPC: H04L12/24 ,  G05B23/02 ,  H04L12/26

    Abstract: According to embodiments, a system, method and non-transitory computer-readable medium are provided to perform fault analysis of time series data including receiving a first set of time series data samples associated with one or more instances of one or more events of a piece of machinery, and receiving one or more labels identifying a relevance of a particular instance to a particular event. One or more event groups is determined based upon the data samples and the labels. Each event group is associated with one or more features of an event. Each data sample of a subset of the data samples is grouped into one of the event groups, and an event pattern is determined based upon the grouping. An event grouping model is constructed based upon the grouping. The event grouping model associates an event pattern with a particular feature of the one or more features of the event.

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