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11.发明申请公开(公告)号：US20220129457A1
公开(公告)日：2022-04-28
申请号：US17081859
申请日：2020-10-27
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Aakash K. Chotrani , Beiwen Guo , Guang C. Wang , Alan P. Wood , Matthew T. Gerdes
IPC: G06F16/2458 , G06N20/00
Abstract: The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal.
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公开(公告)号：US12158548B2
公开(公告)日：2024-12-03
申请号：US17735245
申请日：2022-05-03
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Matthew T. Gerdes , Guang C. Wang , Timothy D. Cline , Kenny C. Gross
Abstract: Systems, methods, and other embodiments associated with acoustic fingerprint identification of devices are described. In one embodiment, a method includes generating a target acoustic fingerprint from acoustic output of a target device. A similarity metric is generated that quantifies similarity of the target acoustic fingerprint to a reference acoustic fingerprint of a reference device. The similarity metric is compared to a threshold. In response to a first comparison result of the comparing of the similarity metric to the threshold, the target device is indicated to match the reference device. In response to a second comparison result of the comparing of the similarity metric to the threshold, it is indicated that the target device does not match the reference device.
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Patent Agency Ranking
公开(公告)号：US11740122B2
公开(公告)日：2023-08-29
申请号：US17506200
申请日：2021-10-20
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Yixiu Liu , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross , Hariharan Balasubramanian
Abstract: Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes partitioning a frequency spectrum of output into a plurality of discrete bins, wherein the output is collected from vibration sensors monitoring a reference device; generating a representative time series signal for each bin while the device is operated in a deterministic stress load; generating a PSD for each bin by converting each signal from the time domain to the frequency domain; determining a maximum power spectral density value and a peak frequency value for each bin; selecting a subset of the bins that have maximum PSD values exceeding a threshold; assigning the representative time series signals from the selected subset of bins as operation vibration signals indicative of operational load on the reference device; and configuring a machine learning model based on at least the operation vibration signals.
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14.发明授权公开(公告)号：US11720823B2
公开(公告)日：2023-08-08
申请号：US17825189
申请日：2022-05-26
Applicant: ORACLE INTERNATIONAL CORPORATION
Inventor： Edward R. Wetherbee , Kenny C. Gross , Guang C. Wang , Matthew T. Gerdes
CPC classification number: G06N20/00 , G06N20/10 , H04L41/0883 , H04L41/16 , H04L41/22 , H04L67/10 , H04L67/12
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.
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15.发明授权公开(公告)号：US11556555B2
公开(公告)日：2023-01-17
申请号：US17081859
申请日：2020-10-27
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Aakash K. Chotrani , Beiwen Guo , Guang C. Wang , Alan P. Wood , Matthew T. Gerdes
IPC: G06F11/00 , G06F16/2458 , G06N20/00 , G06F11/30
Abstract: The disclosed embodiments relate to a system that automatically selects a prognostic-surveillance technique to analyze a set of time-series signals. During operation, the system receives the set of time-series signals obtained from sensors in a monitored system. Next, the system determines whether the set of time-series signals is univariate or multivariate. When the set of time-series signals is multivariate, the system determines if there exist cross-correlations among signals in the set of time-series signals. If so, the system performs subsequent prognostic-surveillance operations by analyzing the cross-correlations. Otherwise, if the set of time-series signals is univariate, the system performs subsequent prognostic-surveillance operations by analyzing serial correlations for the univariate time-series signal.
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公开(公告)号：US20220391754A1
公开(公告)日：2022-12-08
申请号：US17370388
申请日：2021-07-08
Applicant: Oracle International Corporation
Inventor： Beiwen Guo , Matthew T. Gerdes , Guang C. Wang , Hariharan Balasubramanian , Kenny C. Gross
Abstract: The disclosed embodiments relate to a system that produces anomaly-free training data to facilitate ML-based prognostic surveillance operations. During operation, the system receives a dataset comprising time-series signals obtained from a monitored system during normal, but not necessarily fault-free operation of the monitored system. Next, the system divides the dataset into subsets. The system then identifies subsets that contain anomalies by training one or more inferential models using combinations of the subsets, and using the one or more trained inferential models to detect anomalies in other target subsets of the dataset. Finally, the system removes any identified subsets from the dataset to produce anomaly-free training data.
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17.发明申请公开(公告)号：US20220300737A1
公开(公告)日：2022-09-22
申请号：US17205445
申请日：2021-03-18
Applicant: Oracle International Corporation
Inventor： Neelesh Kumar Shukla , Saurabh Thapliyal , Matthew T. Gerdes , Guang C. Wang , Kenny C. Gross
Abstract: The disclosed embodiments provide a system that detects sensor anomalies in a univariate time-series signal. During a surveillance mode, the system receives the univariate time-series signal from a sensor in a monitored system. Next, the system performs a staggered-sampling operation on the univariate time-series signal to produce N sub-sampled time-series signals, wherein the staggered-sampling operation allocates consecutive samples from the univariate time-series signal to the N sub-sampled time-series signals in a round-robin ordering. The system then uses a trained inferential model to generate estimated values for the N sub-sampled time-series signals based on cross-correlations with other sub-sampled time-series signals. Next, the system performs an anomaly detection operation to detect incipient sensor anomalies in the univariate time-series signal based on differences between actual values and the estimated values for the N sub-sampled time-series signals. Whenever an incipient sensor anomaly is detected, the system generates a notification.
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