公开(公告)号：US20240402689A1

公开(公告)日：2024-12-05

申请号：US18203771

申请日：2023-05-31

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Dmitriy ITKIS ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG

IPC: G05B19/418 ,  G06N20/00

Abstract: Systems, methods, and other embodiments associated with quadratic acceleration boost of compute performance for ML prognostics are described. In one embodiment, a prognostic acceleration method includes separating time series signals into a plurality of alternative configurations of clusters based on correlations between the time series signals. Machine learning models are trained for individual clusters in the alternative configurations of clusters. One or more of the alternative configurations of clusters is determined to be viable for use in a production environment based on whether the trained machine learning models for the individual clusters satisfy an accuracy threshold and a completion time threshold. Then, one configuration is selected from the alternative configurations of clusters that were determined to be viable configurations. Production machine learning models are deployed into the production environment to detect anomalies in the time series signals based on the selected configuration.

公开(公告)号：US20230366724A1

公开(公告)日：2023-11-16

申请号：US18223079

申请日：2023-07-18

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Yixiu LIU ,  Matthew T. GERDES ,  Guang C. WANG ,  Kenny C. GROSS ,  Hariharan BALASUBRAMANIAN

IPC: G01H1/00 ,  G06N20/00 ,  G01H17/00

CPC classification number: G01H1/003 ,  G06N20/00 ,  G01H17/00 ,  G01M15/12

Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes automatically choosing a plurality of vibration frequencies that vary in correlation with variation of a load on a monitored device. Vibration amplitudes for the plurality of vibration frequencies are monitored for incipient failure using a machine learning model. The machine learning model is trained to expect the vibration amplitudes to be consistent with undegraded operation of the monitored device. The incipient failure is detected where vibration amplitudes are not consistent with undegraded operation of the monitored device. An alert is then transmitted to suggest maintenance to prevent the incipient failure of the monitored device.

公开(公告)号：US20240256959A1

公开(公告)日：2024-08-01

申请号：US18226522

申请日：2023-07-26

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Kenneth P. BACLAWSKI ,  Richard P. SONDEREGGER ,  Dieter GAWLICK ,  Anna CHYSTIAKOVA ,  Guang Chao WANG ,  Matthew T. GERDES ,  Kenny C. GROSS

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with detecting unfairness in machine learning outcomes are described. In one embodiment, a method includes generating outcomes for transactions with a machine learning tool to be tested for bias. Then, actual values for a test subset of the outcomes that is associated with a test value for a demographic classification are compared with estimated values for the test subset of outcomes. The estimated values are generated by a machine learning model that is trained with a reference subset of the outcomes that are associated with a reference value for the demographic classification. The method then detects whether the machine learning tool is biased or unbiased based on dissimilarity between the actual values and the estimated values for the test subset of the outcomes. The method then generates an electronic alert that the ML tool is biased or unbiased.

公开(公告)号：US20210174248A1

公开(公告)日：2021-06-10

申请号：US16732558

申请日：2020-01-02

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Edward R. WETHERBEE ,  Kenny C. GROSS ,  Guang C. WANG ,  Matthew T. GERDES

IPC: G06N20/00 ,  G06N5/04 ,  H04L12/24 ,  H04L29/08 ,  G06F3/0482

Abstract: 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.

公开(公告)号：US20240354633A1

公开(公告)日：2024-10-24

申请号：US18133125

申请日：2023-04-11

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Matthew T. GERDES ,  Guang Chao WANG ,  Kenny C. GROSS ,  Ruixian LIU

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with determining a quantity of exemplar vectors to select from available training vectors are described. In one embodiment, a method includes determining an available quantity of training vectors that are available in a set of time series signals. A boost function is automatically selected from a plurality of different boost functions based on the available quantity of the training vectors. A selection quantity of the exemplar vectors to select from the training vectors is generated by applying the selected boost function to the training vectors. A quantity of the exemplar vectors is selected from the training vectors based on the selection quantity. A machine learning model is trained to detect an anomaly in the time series signals based on the exemplar vectors that were selected.

公开(公告)号：US20230358597A1

公开(公告)日：2023-11-09

申请号：US18098277

申请日：2023-01-18

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Matthew T. GERDES ,  Guang C. WANG ,  Timothy D. CLINE ,  Kenny C. GROSS

IPC: G01H3/08 ,  G06F17/14 ,  G06F17/18 ,  G08B21/00

CPC classification number: G01H3/08 ,  G06F17/14 ,  G06F17/18 ,  G08B21/00

Abstract: Systems, methods, and other embodiments associated with acoustic detection of changes in mass of cargo carried by a vehicle are described herein. In one example method for acoustic cargo surveillance, a first acoustic output of a vehicle carrying cargo at a first time of surveillance of the vehicle is recorded. Then, a second acoustic output of the vehicle at a subsequent time in the surveillance of the vehicle carrying the cargo is recorded. A change in a mass of the cargo carried by the vehicle is acoustically detected based at least on an acoustic change between the first acoustic output and the second acoustic output. An electronic alert is generated that the mass of the cargo has changed based on the acoustic change.

公开(公告)号：US20230075065A1

公开(公告)日：2023-03-09

申请号：US17463742

申请日：2021-09-01

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Ikenna D. IVENSO ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang C. WANG ,  Hariharan BALASUBRAMANIAN

IPC: G06N20/00

Abstract: Systems, methods, and other embodiments associated with passive inferencing of signal following in multivariate anomaly detection are described. In one embodiment, a method for inferencing signal following in a machine learning (ML) model includes calculating an average standard deviation of measured values of time series signals in a set of time series signals; training the ML model to predict values of the signals; predicting values of each of the signals with the trained ML model; generating a time series set of residuals between the predicted values and the measured values; calculating an average standard deviation of the sets of residuals; determining that signal following is present in the trained ML model where a ratio of the average standard deviation of measured values to the average standard deviation of the sets of residuals exceeds a threshold; and presenting an alert indicating the presence of signal following in the trained ML model.

公开(公告)号：US20220261689A1

公开(公告)日：2022-08-18

申请号：US17382593

申请日：2021-07-22

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： William A. WIMSATT ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang C. WANG

IPC: G06N20/00 ,  G06N5/04 ,  G06K9/00

Abstract: Systems, methods, and other embodiments associated with off-duty-cycle-robust machine learning for anomaly detection in assets with random downtimes are described. In one embodiment, a method includes inferring ranges of asset downtime from spikes in a numerical derivative of a time series signal for an asset; extracting an asset downtime signal from the time series signal based on the inferred ranges of asset downtime; determining that the asset downtime signal carries telemetry based on the variance of the asset downtime signal; training a first machine learning model for the asset downtime signal; detecting a first spike in the numerical derivative of the time signal that indicates a transition to asset downtime; and in response to detection of the first spike, monitoring the time series signal for anomalous activity with the trained first machine learning model.

公开(公告)号：US20250094830A1

公开(公告)日：2025-03-20

申请号：US18370101

申请日：2023-09-19

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Ruixian LIU ,  Kuei-Da LIAO ,  Guang Chao WANG ,  Matthew T. GERDES ,  Kenny C. GROSS

IPC: G06N5/022

Abstract: Systems, methods, and other embodiments associated with clustering of time series signals based on frequency domain analysis are described. In one embodiment, an example method includes accessing time series signals to be separated into clusters. The example method also includes determining similarity in the frequency domain among the time series signals. The example method further includes extracting a cluster of similar time series signals from the time series signals based on the similarity in the frequency domain. And, the example method includes training a machine learning model to detect anomalies based on the cluster.

公开(公告)号：US20240346361A1

公开(公告)日：2024-10-17

申请号：US18133047

申请日：2023-04-11

Applicant: ORACLE INTERNATIONAL CORPORATION

Inventor： Keyang RU ,  Kuei-Da LIAO ,  Matthew T. GERDES ,  Kenny C. GROSS ,  Guang Chao WANG ,  Ruixian LIU

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Systems, methods, and other embodiments associated with automatic clustering of signals including added ambient signals are described. In one embodiment, a method includes receiving time series signals (TSSs) associated with a plurality of machines (or components or other signal sources). The TSSs are unlabeled as to which of the machines the TSSs are associated with. The TSSs are automatically separated into a plurality of clusters corresponding to the plurality of the machines. A group of ambient TSSs is identified that overlaps more than one of the clusters. The group of the ambient TSSs is added into the one cluster of the clusters that corresponds to the one machine. A machine learning model is then trained to detect an anomaly based on the one cluster to generate a trained machine learning model that is specific to the one machine without using the TSSs not included in the one cluster.