INTERPRETABLE PREDICTION USING EXTRACTED TEMPORAL AND TRANSITION RULES

    公开(公告)号:US20210133080A1

    公开(公告)日:2021-05-06

    申请号:US17072526

    申请日:2020-10-16

    Abstract: Methods and systems for detecting and responding to anomalous system behavior include detecting an anomaly in a cyber-physical system, based on a classification of time series information, from sensors that monitor the cyber-physical system, as being anomalous. A transition rule is extracted from the time series information to characterize a cause of the anomalous behavior, using a temporal gradient boosting tree. A corrective action is performed responsive to the detected anomaly, prioritized by the cause of the anomalous behavior.

    OPTIMIZATION OF CYBER-PHYSICAL SYSTEMS
    2.
    发明申请

    公开(公告)号:US20200019858A1

    公开(公告)日:2020-01-16

    申请号:US16508512

    申请日:2019-07-11

    Abstract: Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

    SENSOR CONTRIBUTION RANKING
    3.
    发明申请

    公开(公告)号:US20210103768A1

    公开(公告)日:2021-04-08

    申请号:US17064058

    申请日:2020-10-06

    Abstract: Methods and systems for detecting and correcting anomalies include detecting an anomaly in a cyber-physical system, based on a classification of time series information from sensors that monitor the cyber-physical system as being anomalous. A similarity graph is determined for each of the sensors, based on the time series information. A subset of the sensors that are related to the classification is selected, based on a spectral embedding of the similarity graphs. A corrective action is performed responsive to the detected anomaly, prioritized according to the selected subset.

    Optimization of cyber-physical systems

    公开(公告)号:US11687772B2

    公开(公告)日:2023-06-27

    申请号:US16508512

    申请日:2019-07-11

    CPC classification number: G06N3/08 G06N3/04

    Abstract: Methods and systems for optimizing performance of a cyber-physical system include training a machine learning model, according to sensor data from the cyber-physical system, to generate one or more parameters for controllable sensors in the cyber-physical system that optimize a performance indicator. New sensor data is collected from the cyber-physical system. One or more parameters for the controllable sensors are generated using the trained machine learning module and the new sensor data. The one or more parameters are applied to the controllable sensors to optimize the performance of the cyber-physical system.

    APPROACH TO PREDICTING ENTITY FAILURES THROUGH DECISION TREE MODELING

    公开(公告)号:US20210133017A1

    公开(公告)日:2021-05-06

    申请号:US17075309

    申请日:2020-10-20

    Abstract: Systems and methods for predicting device failure, including inputting a plurality of records for electronic communication devices, each including one or more attributes and a label, as a table to a modeling algorithm, wherein there are separate tables for each period in a time sequence; building a multi-stage decision tree from the time sequence of records using the modeling algorithm running on a processor device; inputting a record for a device having an empty label value into the decision tree to determine the likelihood of entity failure; and reporting a predicted failure for the device to a user on a display to initiate replacement before a next time period.

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