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    Anomaly detection apparatus, anomaly detection method, and recording medium
    1.
    发明授权
    Anomaly detection apparatus, anomaly detection method, and recording medium 有权

    公开(公告)号：US12197200B2

    公开(公告)日：2025-01-14

    申请号：US17760512

    申请日：2019-09-30

    Applicant: NEC Corporation

    Inventor： Mineto Satoh ,  Masatsugu Ogawa

    IPC: G05B23/00 ,  G05B23/02

    Abstract: Provided is an anomaly detection apparatus 10 capable of detecting anomalies of processing results. The anomaly detection apparatus 10 estimates first processing result information in accordance with first processing, using data regarding a target, compares second processing result data, for the data, obtained by second processing that differs from the first processing with the first processing result information, and detects an anomaly of the second processing according to the comparison result.

    Method for obtaining the deformation of a tire under load during running
    2.
    发明授权
    Method for obtaining the deformation of a tire under load during running 有权

    公开(公告)号：US11999204B2

    公开(公告)日：2024-06-04

    申请号：US17416872

    申请日：2019-12-16

    Applicant: COMPAGNIE GENERALE DES ETABLISSEMENTS MICHELIN

    Inventor： Denis Alff

    IPC: G05B23/00 ,  B60C23/06

    CPC classification number: B60C23/061

    Abstract: A method for obtaining the deformation of a tire casing subjected to a load, rotating at a rotational speed W, comprises the following steps: acquiring a signal comprising the amplitude of the acceleration in the direction normal to the crown when running at the rotational speed W; delimiting the signal over a number of wheel revolutions, so as to construct a wheel revolution signal; determining a reference acceleration; defining a first energy density S which is a function of the wheel revolution signal, and of the reference acceleration, and which is denoted S+ when the wheel revolution signal is above a threshold value A, or is denoted S− when the wheel revolution signal is below or equal to said threshold value A; and identifying the deformation generated by the load as a function of the reference acceleration and of the first energy density S.

    Wireless sensor system and related methods
    6.
    发明授权
    Wireless sensor system and related methods 有权

    公开(公告)号：US11586158B1

    公开(公告)日：2023-02-21

    申请号：US16809965

    申请日：2020-03-05

    Applicant: Etellimetrix LLC

    Inventor： Brian D. Downing ,  Pranam Joshi ,  Ramesh Narasimhan ,  Afreen Shaikh

    IPC: G05B9/05 ,  G05B23/00 ,  G01D21/02

    Abstract: Implementations of systems for monitoring industrial equipment may include: a processor coupled with one or more sensors. The systems may include one or more input/outputs coupled with the sensors. The one or more input/outputs may be configured to couple with one or more peripheral devices. The processor may be configured to electrically couple with a remote server. The remote server may be configured to process data received from the one or more sensors and instruct, through the processor, the one or more peripheral devices to make an adjustment.

    Deep causality learning for event diagnosis on industrial time-series data
    8.
    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.

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