University of Connecticut

Toyota Motor Engineering & Manufacturing North America, Inc.

Toyota Technical Center, U.s.a., Inc.

Krishna Pattipatti

Shailesh N Joshi

Ercan Mehment Dede

Ali M Bazzi

Weiqiang Chen

Lingyi Zhang

Liu Qiao

Shunsuke Chigusa

Jianhui Luo

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.

Distributed architecture for fault monitoring

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.

Fault diagnosis using distributed PCA architecture

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.

Fault diagnosis in power electronics using adaptive PCA

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.

Intelligent model-based diagnostics for system monitoring, diagnosis and maintenance

Growing community of inventors

Storrs, CT, United States of America

Krishna Pattipatti