公开(公告)号：US11948051B2

公开(公告)日：2024-04-02

申请号：US16826478

申请日：2020-03-23

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. Wetherbee ,  Kenneth P. Baclawski ,  Guang C. Wang ,  Kenny C. Gross ,  Anna Chystiakova ,  Dieter Gawlick ,  Zhen Hua Liu ,  Richard Paul Sonderegger

IPC: G06F16/00 ,  G05B23/02 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  G06N20/00 ,  G06F111/10 ,  G06N3/08 ,  G06N20/10

CPC classification number: G06N20/00 ,  G05B23/024 ,  G06F17/16 ,  G06F17/18 ,  G06F30/27 ,  G06F2111/10 ,  G06N3/08 ,  G06N20/10

Abstract: In one embodiment, a method for auditing the results of a machine learning model includes: retrieving a set of state estimates for original time series data values from a database under audit; reversing the state estimation computation for each of the state estimates to produce reconstituted time series data values for each of the state estimates; retrieving the original time series data values from the database under audit; comparing the original time series data values pairwise with the reconstituted time series data values to determine whether the original time series and reconstituted time series match; and generating a signal that the database under audit (i) has not been modified where the original time series and reconstituted time series match, and (ii) has been modified where the original time series and reconstituted time series do not match.

公开(公告)号：US11460500B2

公开(公告)日：2022-10-04

申请号：US16784506

申请日：2020-02-07

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. Wetherbee ,  Guang C. Wang ,  Kenny C. Gross ,  Michael Dayringer ,  Andrew Lewis ,  Matthew T. Gerdes

IPC: G01R31/28 ,  G01R31/302 ,  G06F21/55 ,  G06K9/62 ,  G01R31/00 ,  G06K9/00

Abstract: Detecting whether a target device that includes multiple electronic components is genuine or suspected counterfeit by: performing a test sequence of energizing and de-energizing the target device and collecting electromagnetic interference (EMI) signals emitted by the target device; generating a target EMI fingerprint from the EMI signals collected; retrieving a plurality of reference EMI fingerprints from a database library, each of which corresponds to a different configuration of electronic components of a genuine device of the same make and model as the target device; iteratively comparing the target EMI fingerprint to the retrieved reference EMI fingerprints and generating a similarity metric between each compared set; and indicating that the target device (i) is genuine where the similarity metric for any individual reference EMI fingerprint satisfies a threshold test, and is a suspect counterfeit device where no similarity metric for any individual reference EMI fingerprint satisfies the test.

公开(公告)号：US11341588B2

公开(公告)日：2022-05-24

申请号：US16560629

申请日：2019-09-04

Applicant: Oracle International Corporation

Inventor： Edward R. Wetherbee ,  Kenny C. Gross

IPC: G06Q50/00 ,  G06Q50/06 ,  G06F17/18 ,  G06N5/04 ,  G06Q10/06 ,  G06Q10/00

Abstract: During operation, the system receives time-series signals gathered from sensors in a utility system asset. Next, the system uses an inferential model to generate estimated values for the time-series signals, and performs a pairwise differencing operation between actual values and the estimated values for the time-series signals to produce residuals. The system then performs a sequential probability ratio test (SPRT) on the residuals to produce SPRT alarms. Next, the system applies an irrelevance filter to the SPRT alarms to produce filtered SPRT alarms, wherein the irrelevance filter removes SPRT alarms for signals that are uncorrelated with previous failures of similar utility system assets. The system then uses a logistic-regression model to compute an RUL-based risk index for the utility system asset based on the filtered SPRT alarms. When the risk index exceeds a threshold, the system generates a notification indicating that the utility system asset needs to be replaced.

公开(公告)号：US11255894B2

公开(公告)日：2022-02-22

申请号：US16804531

申请日：2020-02-28

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. Wetherbee ,  Rui Zhong ,  Kenny C. Gross ,  Guang C. Wang

IPC: G01R31/00 ,  G01R29/08 ,  G06F21/44

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.

公开(公告)号：US10929776B2

公开(公告)日：2021-02-23

申请号：US16186365

申请日：2018-11-09

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Guang C. Wang ,  Edward R. Wetherbee

IPC: G06N20/00 ,  G06N5/04 ,  G01R31/62

Abstract: During operation, the system obtains time-series sensor signals gathered from sensors in an asset during operation of the asset in an outdoor environment, wherein the time-series sensor signals include temperature signals. Next, the system produces thermally-compensated time-series sensor signals by performing a thermal-compensation operation on the temperature signals to compensate for variations in the temperature signals caused by dynamic variations in an ambient temperature of the outdoor environment. The system then trains a prognostic inferential model for a prognostic pattern-recognition system based on the thermally-compensated time-series sensor signals. During a surveillance mode for the prognostic pattern-recognition system, the system receives recently-generated time-series sensor signals from the asset, and performs a thermal-compensation operation on temperature signals in the recently-generated time-series sensor signals. Finally, the system applies the prognostic inferential model to the thermally-compensated, recently-generated time-series sensor signals to detect incipient anomalies that arise during operation of the asset.

公开(公告)号：US20190293697A1

公开(公告)日：2019-09-26

申请号：US16295613

申请日：2019-03-07

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Edward R. Wetherbee

IPC: G01R19/25 ,  G06N5/04 ,  G06N20/00

Abstract: During a surveillance mode, the system receives present time-series signals gathered from sensors in the power transformer. Next, the system uses an inferential model to generate estimated values for the present time-series signals, and performs a pairwise differencing operation between actual values and the estimated values for the present time-series signals to produce residuals. The system then performs a sequential probability ratio test on the residuals to produce alarms having associated tripping frequencies (TFs). Next, the system uses a logistic-regression model to compute a risk index for the power transformer based on the TFs. If the risk index exceeds a threshold, the system generates a notification that the power transformer needs to be replaced. The system also periodically updates the logistic-regression model based on the results of periodic dissolved gas analyses for the transformer to more accurately compute the index for the power transformer.