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:
Oct. 24, 2023

Filed:

Mar. 26, 2020
Applicant:

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

Inventors:

Nianjun Zhou, Chappaqua, NY (US);

Dhaval Patel, White Plains, NY (US);

Jayant R. Kalagnanam, Briarcliff Manor, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 23/02 (2006.01); G06N 20/00 (2019.01); G06N 5/04 (2023.01);
U.S. Cl.
CPC ...
G05B 23/0283 (2013.01); G05B 23/027 (2013.01); G05B 23/0245 (2013.01); G05B 23/0297 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01);
Abstract

Context-awareness in preventative maintenance is provided by receiving sensor data from a plurality of monitored systems; extracting a first plurality of features from a set of work orders for the monitored systems, wherein individual work orders include a root cause analysis for a context in which a nonconformance in an indicated monitored system occurred; predicting, via a machine learning model, a nonconformance likelihood for each monitored system based on the first plurality of features; selecting a subset of alerts based on predicted nonconformance likelihoods for the monitored systems; in response to receiving a user selection from the first set of alerts and a reason for the user selection, recording the reason as a modifier for the machine learning model; and updating the machine learning model to predict the subsequent nonconformance likelihoods using a second plurality of features that excludes the additional feature identified from the first plurality of features.


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