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. 08, 2020

Filed:

Sep. 26, 2014
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Hyung-il Ahn, San Jose, CA (US);

Matthew Denesuk, Ridgefield, CT (US);

Axel Hochstein, San Jose, CA (US);

Ying Tat Leung, Saratoga, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01M 17/00 (2006.01); G06F 7/00 (2006.01); G06F 11/30 (2006.01); G07C 5/00 (2006.01); B60K 35/00 (2006.01); G05B 23/02 (2006.01); G07C 5/08 (2006.01); B60L 1/00 (2006.01); B60L 3/00 (2019.01); H02G 3/00 (2006.01);
U.S. Cl.
CPC ...
G07C 5/006 (2013.01); B60K 35/00 (2013.01); G05B 23/024 (2013.01); G07C 5/008 (2013.01); G07C 5/0808 (2013.01);
Abstract

Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.


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