Patents by Inventor Alexander B. BATES
Alexander B. BATES 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: 11348018Abstract: A system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations.Type: GrantFiled: December 18, 2018Date of Patent: May 31, 2022Assignee: Aspen Technology, Inc.Inventors: Ashok Rao, Hong Zhao, Pedro Alejandro Castillo Castillo, Mark-John Bruwer, Mir Khan, Alexander B. Bates
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Patent number: 10733536Abstract: A plant asset failure prediction system and associated method. The method includes receiving user input identifying a first target set of equipment including a first plurality of units of equipment. A set of time series waveforms from sensors associated with the first plurality of units of equipment are received, the time series waveforms including sensor data values. A processor is configured to process the time series waveforms to generate a plurality of derived inputs wherein the derived inputs and the sensor data values collectively comprise sensor data. The method further includes determining whether a first machine learning agent may be configured to discriminate between first normal baseline data for the first target set of equipment and first failure signature information for the first target set of equipment.Type: GrantFiled: September 29, 2017Date of Patent: August 4, 2020Assignee: Mtelligence CorporationInventors: Alexander B. Bates, Caroline Kim, Paul Rahilly
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Publication number: 20190188584Abstract: A system that provides an improved approach for detecting and predicting failures in a plant or equipment process. The approach may facilitate failure-model building and deployment from historical plant data of a formidable number of measurements. The system implements methods that generate a dataset containing recorded measurements for variables of the process. The methods reduce the dataset by cleansing bad quality data segments and measurements for uninformative process variables from the dataset. The methods then enrich the dataset by applying nonlinear transforms, engineering calculations and statistical measurements. The methods identify highly correlated input by performing a cross-correlation analysis on the cleansed and enriched dataset, and reduce the dataset by removing less-contributing input using a two-step feature selection procedure. The methods use the reduced dataset to build and train a failure model, which is deployed online to detect and predict failures in real-time plant operations.Type: ApplicationFiled: December 18, 2018Publication date: June 20, 2019Inventors: Ashok Rao, Hong Zhao, Pedro Alejandro Castillo Castillo, Mark-John Bruwer, Mir Khan, Alexander B. Bates
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Patent number: 10192170Abstract: A system for performing failure signature recognition training for at least one unit of equipment. The system includes a memory and a processor coupled to the memory. The processor is configured by computer code to receive sensor data relating to the unit of equipment and to receive failure information relating to equipment failures. The processor is further configured to analyze the sensor data in view of the failure information in order to develop at least one learning agent for performing failure signature recognition with respect to the at least one unit of equipment.Type: GrantFiled: November 30, 2016Date of Patent: January 29, 2019Assignee: MTELLIGENCE CORPORATIONInventors: Alexander B. Bates, Paul Rahilly, Scott Macnab
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Publication number: 20180082217Abstract: A plant asset failure prediction system and associated method. The method includes receiving user input identifying a first target set of equipment including a first plurality of units of equipment. A set of time series waveforms from sensors associated with the first plurality of units of equipment are received, the time series waveforms including sensor data values. A processor is configured to process the time series waveforms to generate a plurality of derived inputs wherein the derived inputs and the sensor data values collectively comprise sensor data. The method further includes determining whether a first machine learning agent may be configured to discriminate between first normal baseline data for the first target set of equipment and first failure signature information for the first target set of equipment.Type: ApplicationFiled: September 29, 2017Publication date: March 22, 2018Inventors: Alexander B. Bates, Caroline Kim, Paul Rahilly
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Patent number: 9842302Abstract: A plant asset failure prediction system and associated method. The method includes receiving user input identifying a first target set of equipment including a first plurality of units of equipment. A set of time series waveforms from sensors associated with the first plurality of units of equipment are received, the time series waveforms including sensor data values. A processor is configured to process the time series waveforms to generate a plurality of derived inputs wherein the derived inputs and the sensor data values collectively comprise sensor data. The method further includes determining whether a first machine learning agent may be configured to discriminate between first normal baseline data for the first target set of equipment and first failure signature information for the first target set of equipment.Type: GrantFiled: August 26, 2015Date of Patent: December 12, 2017Assignee: MTELLIGENCE CORPORATIONInventors: Alexander B. Bates, Caroline Kim, Paul Rahilly
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Publication number: 20170083830Abstract: A system for performing failure signature recognition training for at least one unit of equipment. The system includes a memory and a processor coupled to the memory. The processor is configured by computer code to receive sensor data relating to the unit of equipment and to receive failure information relating to equipment failures. The processor is further configured to analyze the sensor data in view of the failure information in order to develop at least one learning agent for performing failure signature recognition with respect to the at least one unit of equipment.Type: ApplicationFiled: November 30, 2016Publication date: March 23, 2017Inventors: Alexander B. Bates, Paul Rahilly, Scott Macnab
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Patent number: 9535808Abstract: A system for performing failure signature recognition training for at least one unit of equipment. The system includes a memory and a processor coupled to the memory. The processor is configured by computer code to receive sensor data relating to the unit of equipment and to receive failure information relating to equipment failures. The processor is further configured to analyze the sensor data in view of the failure information in order to develop at least one learning agent for performing failure signature recognition with respect to the at least one unit of equipment.Type: GrantFiled: March 17, 2014Date of Patent: January 3, 2017Assignee: Mtelligence CorporationInventors: Alexander B. Bates, Paul Rahilly, Scott Macnab
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Publication number: 20160116378Abstract: A plant asset failure prediction system and associated method. The method includes receiving user input identifying a first target set of equipment including a first plurality of units of equipment. A set of time series waveforms from sensors associated with the first plurality of units of equipment are received, the time series waveforms including sensor data values. A processor is configured to process the time series waveforms to generate a plurality of derived inputs wherein the derived inputs and the sensor data values collectively comprise sensor data. The method further includes determining whether a first machine learning agent may be configured to discriminate between first normal baseline data for the first target set of equipment and first failure signature information for the first target set of equipment.Type: ApplicationFiled: August 26, 2015Publication date: April 28, 2016Inventors: Alexander B. Bates, Caroline Kim, Paul Rahilly
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Publication number: 20140351642Abstract: A system for performing failure signature recognition training for at least one unit of equipment. The system includes a memory and a processor coupled to the memory. The processor is configured by computer code to receive sensor data relating to the unit of equipment and to receive failure information relating to equipment failures. The processor is further configured to analyze the sensor data in view of the failure information in order to develop at least one learning agent for performing failure signature recognition with respect to the at least one unit of equipment.Type: ApplicationFiled: March 17, 2014Publication date: November 27, 2014Applicant: MTELLIGENCE CORPORATIONInventors: Alexander B. BATES, Paul RAHILLY, Scott MACNAB