Oracle International Corporation

Oracle International Corporaiton

Edward R Wetherbee

Kenny C Gross

Guang Chao Wang

Andrew J Lewis

Matthew T Gerdes

Michael Dayringer

Zhen Hua Liu

Dieter Gawlick

Kenneth P Baclawski

Richard Paul Sonderegger

Rui Zhong

Anna Chystiakova

Anna Morav

Systems, methods, and other embodiments associated with auditing the results of a machine learning model are described. In one embodiment, a method accesses original time series data and machine learning estimates of the original time series data. The method generates reconstituted time series data from the machine learning estimates by reversing operations of a machine learning model trained for generating the machine learning estimates from the original time series data. The method detects tampering (or corruption) in the original time series data based on a difference between the original time series data and reconstituted time series data. And, the method generates an electronic verification report that indicates whether the tampering (or corruption) is detected in the original time series data.

System and method for ensuring that the results of machine learning models can be audited

Systems and methods are described that estimates a remaining useful life (RUL) of an electronic device. Time-series signals gathered from sensors in the electronic device are received. Statistical changes are detected in the set of time-series signals that are deemed as anomalous signal patterns. Anomaly alarms are generated, wherein an anomaly alarm is generated for each of the anomalous signal patterns. An irrelevance filter is applied to the set of anomaly alarms to produce filtered anomaly alarms, wherein the irrelevance filter removes anomaly alarms associated with anomalous signal patterns that are not correlated with previous failures of similar electronic devices. A logistic-regression model is used to compute an RUL-based risk index for the electronic device based on the filtered anomaly alarms. When the risk index exceeds a risk-index threshold, a notification is generated indicating that the electronic device has a limited remaining useful life.

Using an irrelevance filter to facilitate efficient RUL analyses for electronic devices

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.

Systems, methods, and other embodiments associated with automated calibration in electromagnetic scanners are described. In one embodiment, a method includes: detecting one or more peak frequency bands in electromagnetic signals collected by the electromagnetic scanner at a geographic location; comparing the one or more peak frequency bands to broadcast frequencies assigned to local radio stations of the geographic location; and indicating that the electromagnetic scanner is calibrated by finding at least one match between one peak frequency band of the peak frequency bands and one of the broadcast frequencies. An electromagnetic scanner may be recalibrated based on comparing the one or more peak frequency bands to broadcast frequencies.

Automated calibration in electromagnetic scanners

Systems, methods, and other embodiments associated with autonomous cloud-node scoping for big-data machine learning use cases are described. In some example embodiments, an automated scoping tool, method, and system are presented that, for each of multiple combinations of parameter values, (i) set a combination of parameter values describing a usage scenario, (ii) execute a machine learning application according to the combination of parameter values on a target cloud environment, and (iii) measure the computational cost for the execution of the machine learning application. A recommendation regarding configuration of central processing unit(s), graphics processing unit(s), and memory for the target cloud environment to execute the machine learning application is generated based on the measured computational costs.

Generating recommended processor-memory configurations for machine learning applications

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.

Kiviat tube based EMI fingerprinting for counterfeit device detection

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.

Counterfeit device detection using EMI fingerprints

Autonomous cloud-node scoping framework for big-data machine learning use cases

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.

Using an irrelevance filter to facilitate efficient RUL analyses for utility system assets

Systems, methods, and other embodiments associated with automated calibration of electromagnetic interference (EMI) fingerprint scanning instrumentation based on radio frequencies are described. In one embodiment, a method for detecting a calibration state of an EMI fingerprint scanning device includes: collecting electromagnetic signals with the EMI fingerprint scanning device for a test period of time at a geographic location; identifying one or more peak frequency bands in the collected electromagnetic signals; comparing the one or more peak frequency bands to assigned radio station frequencies at the geographic location to determine if a match is found; and generating a calibration state signal based at least in part on the comparing to indicate whether the EMI fingerprint scanning device is calibrated or not calibrated.

Automated calibration of EMI fingerprint scanning instrumentation for utility power system counterfeit detection

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.

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

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.

Estimating the remaining useful life of a power transformer based on real-time sensor data and periodic dissolved gas analyses

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.

Thermally-compensated prognostic-surveillance technique for critical assets in outdoor environments

The disclosed embodiments provide a system that detects counterfeit electronic components in a target device, which is part of an electrical generation and distribution system. During operation, the system obtains target EMI signals, which were gathered by monitoring target electromagnetic interference (EMI) emissions generated by the target device using one or more target antennas positioned in proximity to the target device. Next, the system generates a target EMI fingerprint for the target device from the target EMI signals. Finally, the system compares the target EMI fingerprint against a reference EMI fingerprint for the target device to determine whether the target device contains one or more counterfeit electronic components.

Growing community of inventors

Omaha, NE, United States of America

Edward R Wetherbee