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公开(公告)号：US20220138316A1
公开(公告)日：2022-05-05
申请号：US17086855
申请日：2020-11-02
Applicant: Oracle International Corporation
Inventor： Zexi Chen , Kenny C. Gross , Ashin George , Guang C. Wang
Abstract: The disclosed embodiments relate to a system that characterizes susceptibility of an inferential model to follow signal degradation. During operation, the system receives a set of time-series signals associated with sensors in a monitored system during normal fault-free operation. Next, the system trains the inferential model using the set of time-series signals. The system then characterizes susceptibility of the inferential model to follow signal degradation. During this process, the system adds degradation to a signal in the set of time-series signals to produce a degraded signal. Next, the system uses the inferential model to perform prognostic-surveillance operations on the set of time-series signals with the degraded signal. Finally, the system characterizes susceptibility of the inferential model to follow degradation in the signal based on results of the prognostic-surveillance operations.
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32.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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33.发明授权公开(公告)号：US11295012B2
公开(公告)日：2022-04-05
申请号：US16244006
申请日：2019-01-09
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Ashin George
Abstract: The disclosed embodiments relate to a system that determines whether an inferential model is susceptible to spillover false alarms. During operation, the system receives a set of time-series signals from sensors in a monitored system. The system then trains the inferential model using the set of time-series signals. Next, the system tests the inferential model for susceptibility to spillover false alarms by performing the following operations for one signal at a time in the set of time-series signals. First, the system adds degradation to the signal to produce a degraded signal. The system then uses the inferential model to perform prognostic-surveillance operations on the time-series signals with the degraded signal. Finally, the system detects spillover false alarms based on results of the prognostic-surveillance operations.
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公开(公告)号：US20210406374A1
公开(公告)日：2021-12-30
申请号：US16915593
申请日：2020-06-29
Applicant: Oracle International Corporation
Inventor： Guang C. Wang , Kenny C. Gross
Abstract: The disclosed embodiments provide a system that detects unwanted electronic components in a target asset. During operation, the system obtains target electromagnetic interference (EMI) signals by monitoring EMI signals generated by the target asset while the target asset is running a periodic workload. Next, the system generates a target EMI fingerprint from the target EMI signals. The system then applies a compression/dilation technique to time-series signals in the target EMI fingerprint to achieve alignment with corresponding time-series signals in a reference EMI fingerprint to produce a synchronized target EMI fingerprint. Finally, the system compares the synchronized target EMI fingerprint against the reference EMI fingerprint to determine whether the target asset contains any unwanted electronic components.
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35.发明授权公开(公告)号：US11042428B2
公开(公告)日：2021-06-22
申请号：US15601766
申请日：2017-05-22
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Ashwini R. More
Abstract: We disclose a system that optimizes deployment of sensors in a computer system. During operation, the system generates a training data set by gathering a set of n signals from n sensors in the computer system during operation of the computer system. Next, the system uses an inferential model to replace one or more signals in the set of n signals with corresponding virtual signals, wherein the virtual signals are computed based on cross-correlations with unreplaced remaining signals in the set of n signals. Finally, the system generates a design for an optimized version of the computer system, which includes sensors for the remaining signals, but does not include sensors for the replaced signals. During operation, the optimized version of the computer system: computes the virtual signals from the remaining signals; and uses the virtual signals and the remaining signals while performing prognostic pattern-recognition operations to detect incipient anomalies that arise during execution.
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Patent Agency Ranking
公开(公告)号：US10860011B2
公开(公告)日：2020-12-08
申请号：US16371694
申请日：2019-04-01
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Guang C. Wang
Abstract: During operation, the system receives time-series signals from sensors in the asset while the asset is operating. Next, the system obtains real-time environmental parameters for an environment in which the asset is operating. The system then selects an environment-specific inferential model for the asset based on the real-time environmental parameters, wherein the environment-specific inferential model was trained on a golden system while the golden system was operating in an environment that matches the real-time environmental parameters. Next, the system uses the environment-specific inferential model to generate estimated values for the received time-series signals based on correlations among the received time-series signals, and performs a pairwise-differencing operation between actual values and the estimated values for the received time-series signals to produce residuals. Finally, the system determines from the residuals whether the asset is operating correctly.
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37.发明申请公开(公告)号：US20200372385A1
公开(公告)日：2020-11-26
申请号：US16419846
申请日：2019-05-22
Applicant: Oracle International Corporation
Inventor： Guang C. Wang , Kenny C. Gross
Abstract: The disclosed embodiments provide a system that performs seasonality-compensated prognostic-surveillance operations for an asset. During operation, the system obtains time-series sensor signals gathered from sensors in the asset during operation of the asset. Next, the system identifies seasonality modes in the time-series sensor signals. The system then determines frequencies and phase angles for the identified seasonality modes. Next, the system uses the determined frequencies and phase angles to filter out the seasonality modes from the time-series sensor signals to produce seasonality-compensated time-series sensor signals. The system then applies an inferential model to the seasonality-compensated time-series sensor signals to detect incipient anomalies that arise during operation of the asset. Finally, when an incipient anomaly is detected, the system generates a notification regarding the anomaly.
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公开(公告)号：US10740310B2
公开(公告)日：2020-08-11
申请号：US15925427
申请日：2018-03-19
Applicant: Oracle International Corporation
Inventor： Dieter Gawlick , Kenny C. Gross , Zhen Hua Liu , Adel Ghoneimy
IPC: G06F17/00 , G06F16/22 , G06N5/04 , G06N20/00 , G06F16/174 , G06F16/23 , G06F17/40 , G06F11/30 , G06F11/00
Abstract: The disclosed embodiments relate to a system that preprocesses sensor data to facilitate prognostic-surveillance operations. During operation, the system obtains training data from sensors in a monitored system during operation of the monitored system, wherein the training data comprises time-series data sampled from signals produced by the sensors. The system also obtains functional requirements for the prognostic-surveillance operations. Next, the system performs the prognostic-surveillance operations on the training data and determines whether the prognostic-surveillance operations meet the functional requirements when tested on non-training data. If the prognostic-surveillance operations do not meet the functional requirements, the system iteratively applies one or more preprocessing operations to the training data in order of increasing computational cost until the functional requirements are met.
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39.发明申请公开(公告)号：US20200184351A1
公开(公告)日：2020-06-11
申请号：US16215345
申请日：2018-12-10
Applicant: Oracle International Corporation
Inventor： Guang C. Wang , Kenny C. Gross
Abstract: The system receives original time-series signals from sensors in a monitored system. Next, the system detects and removes spikes from the original time-series signals to produce despiked original time-series signals, which involves using the original time-series data to optimize a damping factor, which is applied to a threshold for a spike-detection technique, and using the spike-detection technique with the optimized damping factor to detect the spikes. The system then generates despiked synthetic time-series signals, which are statistically indistinguishable from the despiked original time-series signals. The system also includes synthetic spikes, which have the same temporal, amplitude and width distributions as the spikes in the original time-series signals, in the despiked synthetic time-series signals to produce synthetic time-series signals with spikes. The system uses the synthetic time-series signals with spikes to train an inferential model, and uses the inferential model to perform prognostic-surveillance operations on subsequently-received signals from the monitored system.
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40.发明申请公开(公告)号：US20200151618A1
公开(公告)日：2020-05-14
申请号：US16186365
申请日：2018-11-09
Applicant: Oracle International Corporation
Inventor： Kenny C. Gross , Guang C. Wang , Edward R. Wetherbee
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.
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