Patents by Inventor Kenny C. Gross

Kenny C. Gross has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10452510
    Abstract: The disclosed embodiments relate to a system for performing prognostic surveillance operations on sensor data. During operation, the system obtains a group of signals from sensors in a monitored system during operation of the monitored system. Next, if possible, the system performs a clustering operation, which divides the group of signals into groups of correlated signals. Then, for one or more groups of signals that exceed a specified size, the system randomly partitions the groups of signals into smaller groups of signals. Next, for each group of signals, the system trains an inferential model for a prognostic pattern-recognition system based on signals in the group of signals. Then, for each group of signals, the system uses a prognostic pattern-recognition system in a surveillance mode and the inferential model to detect incipient anomalies that arise during execution of the monitored system.
    Type: Grant
    Filed: October 25, 2017
    Date of Patent: October 22, 2019
    Assignee: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Alan Paul Wood
  • Publication number: 20190318251
    Abstract: After sensors are placed at three or more non-collinear locations on a surface of the component, the system receives time-series signals from the sensors while the component operates on a representative workload. The system then defines one or more triangles on the surface of the component, wherein each triangle is defined by three vertices, which coincide with different sensor locations on the surface of the component. For each triangle, the system applies a barycentric coordinate technique (BCT) to time-series signals received from sensors located at the vertices of the triangle to determine a candidate location within the triangle to place an additional sensor. The system then compares the candidate locations for each of the one or more triangles to determine a globally optimal location for the additional sensor, and a new sensor is placed at this location. This process is repeated until a desired number of sensors are placed.
    Type: Application
    Filed: April 12, 2018
    Publication date: October 17, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Aleksey M. Urmanov
  • Publication number: 20190310781
    Abstract: The disclosed embodiments provide a system that proactively resilvers a disk array when a disk drive in the array is determined to have an elevated risk of failure. The system receives time-series signals associated with the disk array during operation of the disk array. Next, the system analyzes the time-series signals to identify at-risk disk drives that have an elevated risk of failure. If one or more disk drives are identified as being at-risk, the system performs a proactive resilvering operation on the disk array using a background process while the disk array continues to operate using the at-risk disk drives.
    Type: Application
    Filed: April 6, 2018
    Publication date: October 10, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Dieter Gawlick
  • Publication number: 20190310617
    Abstract: The disclosed embodiments relate to a system that removes quantization effects from a set of time-series signals to produce highly accurate approximations of a set of original unquantized signals. During operation, for each time-series signal in the set of time-series signals, the system determines a number of quantization levels (NQL) in the time-series signal. Next, the system performs a fast Fourier transform (FFT) on the time-series signal to produce a set of Fourier modes for the time-series signal. The system then determines an optimal number of Fourier modes (Nmode) to reconstruct the time-series signal based on the determined NQL for the time-series signal. Next, the system selects Nmode largest-amplitude Fourier modes from the set of Fourier modes for the time-series signal. The system then performs an inverse FFT operation using the Nmode largest-amplitude Fourier modes to produce a dequantized time-series signal to be used in place of the time-series signal.
    Type: Application
    Filed: April 6, 2018
    Publication date: October 10, 2019
    Applicant: Oracle International Corporation
    Inventors: Mengying Li, Kenny C. Gross
  • Publication number: 20190303810
    Abstract: The disclosed embodiments relate to a system that facilitates deployment of utility repair crews to nodes in a utility network. During operation, the system determines a node criticality for each node in the utility network based on a network-reliability analysis, which considers interconnections among the nodes in the utility network. The system also determines a node failure probability for each node in the utility network based on historical weather data, historical node failure data and weather forecast information for the upcoming weather event. The system uses the determined node criticalities and the determined node failure probabilities to determine a deployment plan for deploying repair crews to nodes in the utility network in preparation for the upcoming weather event. The system then presents the deployment plan to a person who uses the deployment plan to deploy repair crews to be available to service nodes in the utility network.
    Type: Application
    Filed: March 28, 2018
    Publication date: October 3, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Andrew I. Vakhutinsky, DeJun Li, Bradley R. Williams, Sungpack Hong
  • Publication number: 20190295190
    Abstract: During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, which gather electrical usage data from customers of the utility system. The system uses the set of input signals and a projection technique to produce projected loadshapes, which are associated with electricity usage in the utility system. Next, the system identifies a closest time period in a database containing recent empirically obtained load-related parameters for the utility system, wherein the load-related parameters in the closest time period are closest to a present set of load-related parameters for the utility system. The system then iteratively adjusts the projected loadshapes based on changes indicated by the load-related parameters in the closest time period until a magnitude of adjustments falls below a threshold. Finally, the system predicts electricity demand for the utility system based on the projected loadshapes.
    Type: Application
    Filed: March 15, 2019
    Publication date: September 26, 2019
    Applicant: Oracle International Corporation
    Inventors: Benjamin P. Franklin, JR., Kenny C. Gross
  • Publication number: 20190293697
    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.
    Type: Application
    Filed: March 7, 2019
    Publication date: September 26, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Edward R. Wetherbee
  • Publication number: 20190286725
    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.
    Type: Application
    Filed: March 19, 2018
    Publication date: September 19, 2019
    Applicant: Oracle International Corporation
    Inventors: Dieter Gawlick, Kenny C. Gross, Zhen Hua Liu, Adel Ghoneimy
  • Publication number: 20190243799
    Abstract: The disclosed embodiments relate to a system that facilitates development of machine-learning techniques to perform prognostic-surveillance operations on time-series data from a monitored system, such as a power plant and associated power-distribution system. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in the monitored system. Next, the system decomposes the original time-series signals into deterministic and stochastic components. The system then uses the deterministic and stochastic components to produce synthetic time-series signals, which are statistically indistinguishable from the original time-series signals. Finally, the system enables a developer to use the synthetic time-series signals to develop machine-learning (ML) techniques to perform prognostic-surveillance operations on subsequently received time-series signals from the monitored system.
    Type: Application
    Filed: February 2, 2018
    Publication date: August 8, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Alan Paul Wood, Steven T. Jeffreys, Avishkar Misra, Lawrence L. Fumagalli, JR.
  • Publication number: 20190243407
    Abstract: The disclosed embodiments relate to a system that compactly stores time-series sensor signals. During operation, the system receives original time-series signals comprising sequences of observations obtained from sensors in a monitored system. Next, the system formulizes the original time-series sensor signals to produce a set of equations, which can be used to generate synthetic time-series signals having the same correlation structure and the same stochastic properties as the original time-series signals. Finally, the system stores the formulized time-series sensor signals in place of the original time-series sensor signals.
    Type: Application
    Filed: August 2, 2018
    Publication date: August 8, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Guang C. Wang, Steven T. Jeffreys, Alan Paul Wood, Coleen L. MacMillan
  • Publication number: 20190233305
    Abstract: The disclosed embodiments relate to a system that performs low-temperature desalination. During operation, the system feeds cold saline water through a liquid-cooling system in a computer data center, wherein the cold saline water is used as a coolant, thereby causing the cold saline water to become heated saline water. Next, the system feeds the heated saline water into a vacuum evaporator comprising a water column having a headspace, which is under a negative pressure due to gravity pulling on the heated saline water in the water column. This negative pressure facilitates evaporation of the heated saline water to form water vapor. Finally, the system directs the water vapor through a condenser, which condenses the water vapor to produce desalinated water.
    Type: Application
    Filed: January 31, 2018
    Publication date: August 1, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Sanjeev Sondur
  • Publication number: 20190236162
    Abstract: The disclosed embodiments relate to a system that caches time-series data in a time-series database system. During operation, the system receives the time-series data, wherein the time-series data comprises a series of observations obtained from sensor readings for each signal in a set of signals. Next, the system performs a multivariate memory vectorization (MMV) operation on the time-series data, which selects a subset of observations in the time-series data that represents an underlying structure of the time-series data for individual and multivariate signals that comprise the time-series data. The system then performs a geometric compression aging (GAC) operation on the selected subset of time-series data. While subsequently processing a query involving the time-series data, the system: caches the selected subset of the time-series data in an in-memory database cache in the time-series database system; and accesses the selected subset of the time-series data from the in-memory database cache.
    Type: Application
    Filed: January 31, 2018
    Publication date: August 1, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Dieter Gawlick, Zhen Hua Liu
  • Publication number: 20190197145
    Abstract: The disclosed embodiments relate to a system that certifies provenance of time-series data in a time-series database. During operation, the system retrieves time-series data from the time-series database, wherein the time-series data comprises a sequence of observations comprising sensor readings for each signal in a set of signals. The system also retrieves multivariate state estimation technique (MSET) estimates, which were computed for the time-series data, from the time-series database. Next, the system performs a reverse MSET computation to produce reconstituted time-series data from the MSET estimates. The system then compares the reconstituted time-series data with the time-series data. If the reconstituted time-series data matches the original time-series data, the system certifies provenance for the time-series data.
    Type: Application
    Filed: December 21, 2017
    Publication date: June 27, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Dieter Gawlick, Zhen Hua Liu, Mengying Li
  • Patent number: 10310459
    Abstract: During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter gathers electrical usage data from a customer of the utility system. Next, the system uses the set of input signals to train an inferential model, which learns correlations among the set of input signals, and uses the inferential model to produce a set of inferential signals, wherein an inferential signal is produced for each input signal in the set of input signals. The system then uses a Fourier-based technique to decompose each inferential signal into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals. Finally, the system projects the set of synthesized signals into the future to produce a forecast for the electricity demand.
    Type: Grant
    Filed: September 26, 2017
    Date of Patent: June 4, 2019
    Assignee: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Benjamin P. Franklin, Jr.
  • Publication number: 20190163719
    Abstract: We present a system that performs prognostic surveillance operations based on sensor signals from a power plant and critical assets in the transmission and distribution grid. The system obtains signals comprising time-series data obtained from sensors during operation of the power plant and associated transmission grid. The system uses an inferential model trained on previously received signals to generate estimated values for the signals. The system then performs a pairwise differencing operation between actual values and the estimated values for the signals to produce residuals. The system subsequently performs a sequential probability ratio test (SPRT) on the residuals to detect incipient anomalies that arise during operation of the power plant and associated transmission grid. While performing the SPRT, the system dynamically updates SPRT parameters to compensate for non-Gaussian artifacts that arise in the sensor data due to changing operating conditions.
    Type: Application
    Filed: November 29, 2017
    Publication date: May 30, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Tahereh Masoumi
  • Publication number: 20190154494
    Abstract: The disclosed embodiments relate to a system that detects degradation in one or more rotating components in a monitored system. During operation, the system receives one or more telemetry signals comprising vibration sensor readings from one or more vibration sensors in the monitored system. The system then performs a fast Fourier transform (FFT) on the vibration sensor readings to produce a power spectral density (PSD) distribution. Next, the system identifies a peak in the PSD distribution, wherein the peak is associated with a target rotating component in the monitored system. After identifying the peak, the system computes a full width half maximum (FWHM) value for a curve associated with the peak. Finally, if the FWHM value exceeds a pre-specified threshold, the system generates a notification about degradation of the target rotating component in the monitored system.
    Type: Application
    Filed: November 22, 2017
    Publication date: May 23, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Aleksey M. Urmanov
  • Publication number: 20190121714
    Abstract: The disclosed embodiments relate to a system for performing prognostic surveillance operations on sensor data. During operation, the system obtains a group of signals from sensors in a monitored system during operation of the monitored system. Next, if possible, the system performs a clustering operation, which divides the group of signals into groups of correlated signals. Then, for one or more groups of signals that exceed a specified size, the system randomly partitions the groups of signals into smaller groups of signals. Next, for each group of signals, the system trains an inferential model for a prognostic pattern-recognition system based on signals in the group of signals. Then, for each group of signals, the system uses a prognostic pattern-recognition system in a surveillance mode and the inferential model to detect incipient anomalies that arise during execution of the monitored system.
    Type: Application
    Filed: October 25, 2017
    Publication date: April 25, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Alan Paul Wood
  • Patent number: 10248561
    Abstract: The disclosed embodiments provide a system that detects anomalous events in a virtual machine. During operation, the system obtains time-series garbage-collection (GC) data collected during execution of a virtual machine in a computer system. Next, the system generates one or more seasonal features from the time-series GC data. The system then uses a sequential-analysis technique to analyze the time-series GC data and the one or more seasonal features for an anomaly in the GC activity of the virtual machine. Finally, the system stores an indication of a potential out-of-memory (OOM) event for the virtual machine based at least in part on identifying the anomaly in the GC activity of the virtual machine.
    Type: Grant
    Filed: June 18, 2015
    Date of Patent: April 2, 2019
    Assignee: ORACLE INTERNATIONAL CORPORATION
    Inventors: Dustin R. Garvey, Sampanna S. Salunke, Lik Wong, Xuemei Gao, Yongqiang Zhang, Eric S. Chan, Kenny C. Gross
  • Publication number: 20190094822
    Abstract: During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter gathers electrical usage data from a customer of the utility system. Next, the system uses the set of input signals to train an inferential model, which learns correlations among the set of input signals, and uses the inferential model to produce a set of inferential signals, wherein an inferential signal is produced for each input signal in the set of input signals. The system then uses a Fourier-based technique to decompose each inferential signal into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals. Finally, the system projects the set of synthesized signals into the future to produce a forecast for the electricity demand.
    Type: Application
    Filed: September 26, 2017
    Publication date: March 28, 2019
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Mengying Li, Benjamin P. Franklin, JR.
  • Publication number: 20180336483
    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.
    Type: Application
    Filed: May 22, 2017
    Publication date: November 22, 2018
    Applicant: Oracle International Corporation
    Inventors: Kenny C. Gross, Ashwini R. More