Patents by Inventor Guruprasad SRINIVASAN
Guruprasad SRINIVASAN 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).
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Patent number: 11868906Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.Type: GrantFiled: December 15, 2020Date of Patent: January 9, 2024Assignee: Utopus Insights, Inc.Inventors: Guruprasad Srinivasan, Younghun Kim, Tarun Kumar
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Publication number: 20230252307Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.Type: ApplicationFiled: April 18, 2023Publication date: August 10, 2023Applicant: Utopus Insights, Inc.Inventors: Younghun Kim, Guruprasad Srinivasan, Tarun Kumar
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Patent number: 11663496Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.Type: GrantFiled: December 21, 2018Date of Patent: May 30, 2023Assignee: Utopus Insights, Inc.Inventors: Younghun Kim, Guruprasad Srinivasan, Tarun Kumar
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Publication number: 20230152794Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.Type: ApplicationFiled: November 4, 2022Publication date: May 18, 2023Applicant: Utopus Insights, Inc.Inventors: Guruprasad Srinivasan, Younghun Kim
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Patent number: 11493911Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.Type: GrantFiled: December 27, 2018Date of Patent: November 8, 2022Assignee: Utopus Insights, Inc.Inventors: Guruprasad Srinivasan, Younghun Kim
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Publication number: 20210216883Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.Type: ApplicationFiled: December 15, 2020Publication date: July 15, 2021Inventors: Guruprasad Srinivasan, Younghun Kim, Tarun Kumar
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Patent number: 10867250Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.Type: GrantFiled: December 27, 2018Date of Patent: December 15, 2020Assignee: Utopus Insights, Inc.Inventors: Guruprasad Srinivasan, Younghun Kim, Tarun Kumar
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Publication number: 20200209841Abstract: An example method comprises receiving historical sensor data of a renewable energy asset for a first time period, identifying historical log data in one or more log sources, retrieving dates of the identified historical log data, retrieving sequences of historical sensor data using the dates, training hidden Markov models using the sequences of historical sensor data to identify probability of shifting states of one or more components of the renewable energy asset, receiving current sensor data of a second time period, identifying current log data in the one or more log sources, retrieving dates of the identified current log data, retrieving sequences of current sensor data using the dates, applying the hidden Markov models to the sequences of the current sensor data to assess likelihood of the one or more faults, creating a prediction of a future fault, and generating a report including the prediction of the future fault.Type: ApplicationFiled: December 27, 2018Publication date: July 2, 2020Applicant: Utopus Insights, Inc.Inventors: Guruprasad Srinivasan, Younghun Kim
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Publication number: 20200210854Abstract: An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.Type: ApplicationFiled: December 27, 2018Publication date: July 2, 2020Applicant: Utopus Insights, Inc.Inventors: Guruprasad Srinivasan, Younghun Kim, Tarun Kumar
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Patent number: 10466661Abstract: A controller includes a processor and memory. The memory stores instructions that, when executed, are configured to cause the processor to receive measurements pertaining to a measured operation parameter of at least a portion of a turbine system. The instructions are also configured to cause the processor to generate a customized model for the at least the portion of the turbine system. Moreover, the instructions are configured to cause the processor to estimate an estimated value using the received measurements. The estimated value pertains to a parameter of the turbine system. Furthermore, the instructions are configured to cause the processor to using the customized model, reduce or remove at least some environmental conditions from a corrected estimated value derived from the estimated value.Type: GrantFiled: December 15, 2016Date of Patent: November 5, 2019Assignee: General Electric CompanyInventors: Atanu Talukdar, Venkatesh Kattigari Madyastha, Guruprasad Srinivasan, Kotesh Kummamuri Rao, Jose Leon Vega, Jose Mendoza, Hardev Singh, Yan Chen, Johan Michael Reimann
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Publication number: 20190236456Abstract: An example method comprises receiving first historical data of a first time period and failure data, identifying at least some sensor data that was or potentially was generated during a first failure, removing the at least some sensor data to create filtered historical data, training a classification model using the filtered historical data, the classification model indicating at least one first classified state at a second period of time prior to the first failure indicated by the failure data, applying the classification model to second sensor data to identify a first potential failure state based on the at least one first classified state, the second sensor data being from a subsequent time period, generating an alert if the first potential failure state is identified based on at least a first subset of sensor signals generated during the subsequent time period, and providing the alert.Type: ApplicationFiled: December 21, 2018Publication date: August 1, 2019Applicant: Utopus Insights, Inc.Inventors: Younghun Kim, Guruprasad Srinivasan, Tarun Kumar
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Publication number: 20170175567Abstract: A controller includes a processor and memory. The memory stores instructions that, when executed, are configured to cause the processor to receive measurements pertaining to a measured operation parameter of at least a portion of a turbine system. The instructions are also configured to cause the processor to generate a customized model for the at least the portion of the turbine system. Moreover, the instructions are configured to cause the processor to estimate an estimated value using the received measurements. The estimated value pertains to a parameter of the turbine system. Furthermore, the instructions are configured to cause the processor to using the customized model, reduce or remove at least some environmental conditions from a corrected estimated value derived from the estimated value.Type: ApplicationFiled: December 15, 2016Publication date: June 22, 2017Inventors: Atanu TALUKDAR, Venkatesh Kattigari MADYASTHA, Guruprasad SRINIVASAN, Kotesh Kummamuri RAO, Jose Leon VEGA, Jose MENDOZA, Hardev SINGH, Yan CHEN, Johan Michael REIMANN