ADAPTIVE FAULT DIAGNOSTIC SYSTEM FOR MOTOR VEHICLES

    公开(公告)号:US20210056780A1

    公开(公告)日:2021-02-25

    申请号:US16548157

    申请日:2019-08-22

    Abstract: A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.

    Battery health monitoring and failure identification

    公开(公告)号:US11981228B2

    公开(公告)日:2024-05-14

    申请号:US17698204

    申请日:2022-03-18

    CPC classification number: B60L58/16 G01R31/367 G01R31/392

    Abstract: A system for monitoring a battery assembly includes a processing device configured to receive measurement data from a plurality of battery components, and input the measurement data to a battery model configured to determine parametric data. Based on the battery model, the processing device is configured to acquire the parametric data, extract statistical information based on at least one parameter of each battery component, and input the statistical information to a failure identification module that includes a first classifier configured to determine whether the battery assembly is in a failure condition based on the statistical information. The processing device is configured to output a health indicator having a first value indicating that the battery assembly is healthy based on first classifier determining that the battery assembly is in the healthy condition, and a faulty value based on the first classifier determining that the battery assembly is in a failure condition.

    BATTERY HEALTH MONITORING AND FAILURE IDENTIFICATION

    公开(公告)号:US20230294554A1

    公开(公告)日:2023-09-21

    申请号:US17698204

    申请日:2022-03-18

    CPC classification number: B60L58/16 G01R31/367 G01R31/392

    Abstract: A system for monitoring a battery assembly includes a processing device configured to receive measurement data from a plurality of battery components, and input the measurement data to a battery model configured to determine parametric data. Based on the battery model, the processing device is configured to acquire the parametric data, extract statistical information based on at least one parameter of each battery component, and input the statistical information to a failure identification module that includes a first classifier configured to determine whether the battery assembly is in a failure condition based on the statistical information. The processing device is configured to output a health indicator having a first value indicating that the battery assembly is healthy based on first classifier determining that the battery assembly is in the healthy condition, and a faulty value based on the first classifier determining that the battery assembly is in a failure condition.

    Adaptive fault diagnostic system for motor vehicles

    公开(公告)号:US11551488B2

    公开(公告)日:2023-01-10

    申请号:US16548157

    申请日:2019-08-22

    Abstract: A method of using an adaptive fault diagnostic system for motor vehicles is provided. A diagnostic tool collects unlabeled data associated with a motor vehicle, and the unlabeled data is transmitted to a central computer. An initial diagnostic model and labeled training data associated with previously identified failure modes and known health conditions are transmitted to the central computer. The central computer executes a novelty detection technique to determine whether the unlabeled data is novelty data corresponding with a new failure mode or normal data corresponding with one of the previously identified failure modes or known health conditions. The central computer selects an informative sample from the novelty data. A repair technician inputs a label for the informative sample, and the central computer propagates the label from the informative sample to the associated novelty data. The central computer updates the labeled training data to include the labeled novelty data.

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