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:
Mar. 15, 2022

Filed:

May. 09, 2019
Applicants:

Fanuc Corporation, Yamanashi, JP;

Preferred Networks, Inc., Tokyo, JP;

Inventors:

Shougo Inagaki, Yamanashi, JP;

Hiroshi Nakagawa, Yamanashi, JP;

Daisuke Okanohara, Tokyo, JP;

Ryosuke Okuta, Tokyo, JP;

Eiichi Matsumoto, Tokyo, JP;

Keigo Kawaai, Tokyo, JP;

Assignees:

FANUC CORPORATION, Yamanashi, JP;

PREFERRED NETWORKS, INC., Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 13/02 (2006.01); G06N 20/00 (2019.01); B25J 9/16 (2006.01); G05B 15/02 (2006.01); G05B 19/4063 (2006.01); G05B 23/02 (2006.01); G06N 3/04 (2006.01); G06N 3/08 (2006.01); G06N 5/04 (2006.01);
U.S. Cl.
CPC ...
G05B 13/0265 (2013.01); B25J 9/163 (2013.01); G05B 15/02 (2013.01); G05B 19/4063 (2013.01); G05B 23/024 (2013.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06N 5/048 (2013.01); G06N 20/00 (2019.01); G05B 2219/31359 (2013.01); G05B 2219/33321 (2013.01); G06N 3/0445 (2013.01); G06N 3/084 (2013.01); Y02P 90/02 (2015.11); Y10S 901/47 (2013.01);
Abstract

A fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.


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