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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.

    Industrial asset temporal anomaly detection with fault variable ranking
    10.
    发明授权
    Industrial asset temporal anomaly detection with fault variable ranking 有权

    公开(公告)号：US11320813B2

    公开(公告)日：2022-05-03

    申请号：US16170699

    申请日：2018-10-25

    Applicant: General Electric Company

    Inventor： Hao Huang

    IPC: G05B23/02

    Abstract: A method of temporal anomaly detection includes accessing sensor data readings obtained at a monitored industrial asset, performing a data cleanup operation on at least a portion of the accessed sensor data readings, transforming at least the cleaned-up portion of the accessed sensor data readings to time series feature space sensor data, applying a multi-kernel-based projection algorithm to the time series feature space sensor data, computing a respective anomaly score and a respective ranking for one or more variables of the sensor data readings, and providing at least the computed respective anomaly score or the respective ranking for at least one of the one or more variables to a user. Ranking the anomaly score includes comparing each anomaly score to a threshold and then assigning a ranking to scores with a magnitude greater than the threshold based on its magnitude. A system and a non-transitory computer-readable medium are also disclosed.

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