ADAPTIVE SEQUENTIAL PROBABILITY RATIO TEST TO FACILITATE A ROBUST REMAINING USEFUL LIFE ESTIMATION FOR CRITICAL ASSETS

    公开(公告)号:US20200272140A1

    公开(公告)日:2020-08-27

    申请号:US16282087

    申请日:2019-02-21

    Abstract: The system receives a set of present time-series signals gathered from sensors in the asset. Next, the system uses an inferential model to generate estimated values for the set of present time-series signals, and performs a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals. The system then performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms with associated tripping frequency (TF). While the TF exceeds a TF threshold, the system iteratively adjusts sensitivity parameters for the SPRT to reduce the TF, and performs the SPRT again on the residuals. The system then uses a logistic regression model to compute a risk index for the asset based on the TF. If the risk index exceeds a threshold, the system generates a notification indicating that the asset needs to be replaced.

    Adaptive sequential probability ratio test to facilitate a robust remaining useful life estimation for critical assets

    公开(公告)号:US11307569B2

    公开(公告)日:2022-04-19

    申请号:US16282087

    申请日:2019-02-21

    Abstract: The system receives a set of present time-series signals gathered from sensors in the asset. Next, the system uses an inferential model to generate estimated values for the set of present time-series signals, and performs a pairwise differencing operation between actual values and the estimated values for the set of present time-series signals to produce residuals. The system then performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms with associated tripping frequency (TF). While the TF exceeds a TF threshold, the system iteratively adjusts sensitivity parameters for the SPRT to reduce the TF, and performs the SPRT again on the residuals. The system then uses a logistic regression model to compute a risk index for the asset based on the TF. If the risk index exceeds a threshold, the system generates a notification indicating that the asset needs to be replaced.

    Optimally deploying utility repair assets to minimize power outages during major weather events

    公开(公告)号:US11010694B2

    公开(公告)日:2021-05-18

    申请号:US15938988

    申请日:2018-03-28

    Abstract: The disclosed embodiments relate to a system that facilitates deployment of utility repair crews to nodes in a utility network. During operation, the system determines a node criticality for each node in the utility network based on a network-reliability analysis, which considers interconnections among the nodes in the utility network. The system also determines a node failure probability for each node in the utility network based on historical weather data, historical node failure data and weather forecast information for the upcoming weather event. The system uses the determined node criticalities and the determined node failure probabilities to determine a deployment plan for deploying repair crews to nodes in the utility network in preparation for the upcoming weather event. The system then presents the deployment plan to a person who uses the deployment plan to deploy repair crews to be available to service nodes in the utility network.

    OPTIMALLY DEPLOYING UTILITY REPAIR ASSETS TO MINIMIZE POWER OUTAGES DURING MAJOR WEATHER EVENTS

    公开(公告)号:US20190303810A1

    公开(公告)日:2019-10-03

    申请号:US15938988

    申请日:2018-03-28

    Abstract: The disclosed embodiments relate to a system that facilitates deployment of utility repair crews to nodes in a utility network. During operation, the system determines a node criticality for each node in the utility network based on a network-reliability analysis, which considers interconnections among the nodes in the utility network. The system also determines a node failure probability for each node in the utility network based on historical weather data, historical node failure data and weather forecast information for the upcoming weather event. The system uses the determined node criticalities and the determined node failure probabilities to determine a deployment plan for deploying repair crews to nodes in the utility network in preparation for the upcoming weather event. The system then presents the deployment plan to a person who uses the deployment plan to deploy repair crews to be available to service nodes in the utility network.

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