Patents by Inventor Krishna Pattipatti
Krishna Pattipatti 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: 11573877Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.Type: GrantFiled: July 16, 2021Date of Patent: February 7, 2023Assignees: Toyota Motor Engineering & Manufacturing North America, Inc., University of ConnecticutInventors: Ercan M. Dede, Shailesh N. Joshi, Lingyi Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi
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Publication number: 20210342244Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.Type: ApplicationFiled: July 16, 2021Publication date: November 4, 2021Applicants: Toyota Motor Engineering & Manufacturing North America, Inc., University of ConnecticutInventors: Ercan M. Dede, Shallesh N. Joshi, Lingyl Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi
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Patent number: 11113168Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof. Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.Type: GrantFiled: March 9, 2018Date of Patent: September 7, 2021Assignees: Toyota Motor Engineering & Manufacturing North America, Inc., University of ConnecticutInventors: Ercan Mehment Dede, Shailesh N. Joshi, Lingyi Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi
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Patent number: 10650616Abstract: A system includes a vehicle having an electronic device, a sensor designed to detect sensor data corresponding to at least one property of the electronic device, an output device designed to output data, and a vehicle network access device designed to transmit the sensor data. The system also includes a machine learning server separate from the vehicle and having a machine learning processor designed to receive the sensor data, and generate, using a machine learning algorithm, a model of the electronic device. The machine learning processor is also designed to determine that a fault is likely to occur with the electronic device by conducting a T squared statistical analysis of the sensor data using the model, and generate a signal to be transmitted to the vehicle network access device when the fault is likely to occur and output information indicating that the fault is likely to occur.Type: GrantFiled: April 6, 2018Date of Patent: May 12, 2020Assignees: UNIVERSITY OF CONNECTICUT, TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.Inventors: Lingyi Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi, Shailesh N. Joshi, Ercan M. Dede
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Publication number: 20190311552Abstract: A system includes a vehicle having an electronic device, a sensor designed to detect sensor data corresponding to at least one property of the electronic device, an output device designed to output data, and a vehicle network access device designed to transmit the sensor data. The system also includes a machine learning server separate from the vehicle and having a machine learning processor designed to receive the sensor data, and generate, using a machine learning algorithm, a model of the electronic device. The machine learning processor is also designed to determine that a fault is likely to occur with the electronic device by conducting a T squared statistical analysis of the sensor data using the model, and generate a signal to be transmitted to the vehicle network access device when the fault is likely to occur and output information indicating that the fault is likely to occur.Type: ApplicationFiled: April 6, 2018Publication date: October 10, 2019Inventors: Lingyi Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi, Shailesh N. Joshi, Ercan M. Dede
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Publication number: 20190278684Abstract: Systems and methods for detecting an anomaly in a power semiconductor device are disclosed. A system includes a server computing device and one or more local components communicatively coupled to the server computing device. Each local component includes sensors positioned adjacent to the power semiconductor device for sensing properties thereof Each local component receives data corresponding to one or more sensed properties of the power semiconductor device from the sensors and transmits the data to the server computing device. The server computing device utilizes the data, via a machine learning algorithm, to generate a set of eigenvalues and associated eigenvectors and select a selected set of eigenvalues and associated eigenvectors. Each local component conducts a statistical analysis of the selected set of eigenvalues and associated eigenvectors to determine that the data is indicative of the anomaly.Type: ApplicationFiled: March 9, 2018Publication date: September 12, 2019Inventors: Ercan Mehment Dede, Shailesh N. Joshi, Lingyi Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi
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Patent number: 10354462Abstract: A system includes an electronic device and a sensor to detect sensor data corresponding to the electronic device. The system also includes a machine learning processor that receives the sensor data and generates a model of the electronic device to determine a T squared threshold and a Q threshold using a machine learning algorithm. The machine learning processor also performs a T squared analysis of the electronic device by comparing a T squared value to the T squared threshold, and a Q analysis of the electronic device by comparing a Q value to the Q threshold. The machine learning processor also determines that the model is faulty when the T squared value is less than the T squared threshold and the Q value is greater than or equal to the Q threshold, and generates a new model or updates the model when the model is determined to be faulty.Type: GrantFiled: April 6, 2018Date of Patent: July 16, 2019Assignees: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC., UNIVERSITY OF CONNECTICUTInventors: Lingyi Zhang, Weiqiang Chen, Krishna Pattipatti, Ali M. Bazzi, Shailesh N. Joshi, Ercan M. Dede
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Publication number: 20080004840Abstract: Systems and methods are provided for monitoring, diagnosis and condition-based maintenance of mechanical systems. The disclosed systems and methods employ intelligent model-based diagnostic methodologies to effectuate such monitoring, diagnosis and maintenance. According to exemplary embodiments of the present disclosure, the intelligent model-based diagnostic methodologies combine or integrate quantitative (analytical) models and graph-based dependency models to enhance diagnostic performance. The disclosed systems and methods may be employed a wide variety of applications, including automotive, aircraft, power systems, manufacturing systems, chemical processes and systems, transportation systems, and industrial machines/equipment.Type: ApplicationFiled: February 28, 2007Publication date: January 3, 2008Inventors: Krishna Pattipatti, Jianhui Luo, Liu Qiao, Shunsuke Chigusa
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Publication number: 20060064291Abstract: Systems and methods are provided for monitoring, diagnosis and condition-based maintenance of mechanical systems. The disclosed systems and methods employ intelligent model-based diagnostic methodologies to effectuate such monitoring, diagnosis and maintenance. According to exemplary embodiments of the present disclosure, the intelligent model-based diagnostic methodologies combine or integrate quantitative (analytical) models and graph-based dependency models to enhance diagnostic performance. The disclosed systems and methods may be employed a wide variety of applications, including automotive, aircraft, power systems, manufacturing systems, chemical processes and systems, transportation systems, and industrial machines/equipment.Type: ApplicationFiled: April 21, 2005Publication date: March 23, 2006Inventors: Krishna Pattipatti, Jianhui Luo, Liu Qiao, Shunsuke Chigusa