Kiviat tube based EMI fingerprinting for counterfeit device detection

    公开(公告)号:US11686756B2

    公开(公告)日:2023-06-27

    申请号:US17672928

    申请日:2022-02-16

    CPC classification number: G01R31/002 G01R29/0814 G01R29/0878 G06F21/44

    Abstract: Detecting a counterfeit status of a target device by: selecting a set of frequencies that best reflect load dynamics or other information content of a reference device while undergoing a power test sequence; obtaining target electromagnetic interference (EMI) signals emitted by the target device while undergoing the same power test sequence; creating a sequence of target kiviat plots from the amplitude of the target EMI signals at each of the set of frequencies at observations over the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint to a reference kiviat tube EMI fingerprint for the reference device undergoing the power test sequence to determine whether the target device and the reference device are of the same type; and generating a signal to indicate a counterfeit status based at least in part on the results of the comparison.

    High sensitivity detection and identification of counterfeit components in utility power systems via EMI frequency kiviat tubes

    公开(公告)号:US11255894B2

    公开(公告)日:2022-02-22

    申请号:US16804531

    申请日:2020-02-28

    Abstract: Detecting a counterfeit status of a target utility device by: selecting a set of frequencies that best reflect load dynamics or other information content of a reference utility device while undergoing a power test sequence; obtaining target electromagnetic interference (EMI) signals emitted by the target utility device while undergoing the same power test sequence; creating a sequence of target kiviat plots from the amplitude of the target EMI signals at each of the set of frequencies at observations over the power test sequence to form a target kiviat tube EMI fingerprint; comparing the target kiviat tube EMI fingerprint to a reference kiviat tube EMI fingerprint for the reference utility device undergoing the power test sequence to determine whether the target utility device and the reference utility device are of the same type; and generating a signal to indicate a counterfeit status based at least in part on the results of the comparison.

    Using a double-blind challenge to evaluate machine-learning-based prognostic-surveillance techniques

    公开(公告)号:US12038830B2

    公开(公告)日:2024-07-16

    申请号:US17090151

    申请日:2020-11-05

    CPC classification number: G06F11/3688 G06F11/3692 G06F21/602 G06N20/00

    Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.

    USING A DOUBLE-BLIND CHALLENGE TO EVALUATE MACHINE-LEARNING-BASED PROGNOSTIC-SURVEILLANCE TECHNIQUES

    公开(公告)号:US20220138090A1

    公开(公告)日:2022-05-05

    申请号:US17090151

    申请日:2020-11-05

    Abstract: A double-blind comparison is performed between prognostic-surveillance systems, which are located on a local system and a remote system. During operation, the local system inserts random faults into a dataset to produce a locally seeded dataset, wherein the random faults are inserted into random signals at random times with variable fault signatures. Next, the local system exchanges the locally seeded dataset with a remote system, and in return receives a remotely seeded dataset, which was produced by the remote system by inserting different random faults into the same dataset. Next, the local system uses a local prognostic-surveillance system to analyze the remotely seeded dataset to produce locally detected faults. Finally, the local system determines a performance of the local prognostic-surveillance system by comparing the locally detected faults against actual faults in the remotely seeded fault information. The remote system similarly determines a performance of a remote prognostic-surveillance system.

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