The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 07, 2021

Filed:

Mar. 09, 2018
Applicants:

Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US);

University of Connecticut, Storrs, CT (US);

Inventors:

Ercan Mehment Dede, Ann Arbor, MI (US);

Shailesh N. Joshi, Ann Arbor, MI (US);

Lingyi Zhang, Willington, CT (US);

Weiqiang Chen, Willington, CT (US);

Krishna Pattipatti, Storrs, CT (US);

Ali M. Bazzi, South Windsor, CT (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 11/00 (2006.01); G06F 11/30 (2006.01); H04W 4/38 (2018.01); G06F 17/18 (2006.01); G06F 11/07 (2006.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06F 11/3062 (2013.01); G06F 11/0706 (2013.01); G06F 17/18 (2013.01); G06N 20/00 (2019.01); H04W 4/38 (2018.02);
Abstract

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.


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