AUTOMATED MODELING OF AN INDUSTRIAL NETWORK

    公开(公告)号:US20250055760A1

    公开(公告)日:2025-02-13

    申请号:US18231372

    申请日:2023-08-08

    Abstract: In one embodiment, a process discovers network topology information of a particular computer network and creates a plurality of zones of devices in the particular computer network based on the network topology information. The process also discovers network communication activity patterns and endpoints of the particular computer network and creates a plurality of conduits between devices of the particular computer network based on the network communication activity patterns and endpoints of the particular computer network and association of the devices within the plurality of zones as described above.

    IOT-BASED NETWORK ARCHITECTURE FOR DETECTING FAULTS USING VIBRATION MEASUREMENT DATA

    公开(公告)号:US20210133559A1

    公开(公告)日:2021-05-06

    申请号:US16672696

    申请日:2019-11-04

    Abstract: In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

    IoT-based network architecture for detecting faults using vibration measurement data

    公开(公告)号:US11544557B2

    公开(公告)日:2023-01-03

    申请号:US16672696

    申请日:2019-11-04

    Abstract: In one embodiment, a device in a network receives a machine learning encoder and decoder trained by a supervisory service. The service trains the encoder and decoder using vibration measurement data sent to the service by a plurality of devices. The device trains, based on the received encoder, a classifier to determine whether vibration measurement data is indicative of a behavioral anomaly. The device receives vibration measurement data captured by a particular set of one or more vibration sensors of a monitored system. The device evaluates, using the trained decoder, the received vibration measurement data to determine whether the data is indicative of a structural anomaly in the monitored system. The device evaluates, using the trained classifier, the received vibration measurement data to determine whether the data is indicative of a behavioral anomaly in the monitored system.

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