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:
Jul. 08, 2025

Filed:

Nov. 14, 2019
Applicant:

Strong Force Iot Portfolio 2016, Llc, Santa Monica, CA (US);

Inventors:

Charles Howard Cella, Pembroke, MA (US);

Gerald William Duffy, Jr., Philadelphia, PA (US);

Jeffrey P. McGuckin, Philadelphia, PA (US);

Mehul Desai, Oak Brook, IL (US);

Assignee:

STRONG FORCE IOT PORTFOLIO 2016, LLC, Fort Lauderdale, FL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 23/02 (2006.01); G01M 13/028 (2019.01); G01M 13/045 (2019.01); G05B 13/02 (2006.01); G05B 19/418 (2006.01); G06F 16/24 (2019.01); G06F 18/21 (2023.01); G06N 3/006 (2023.01); G06N 3/02 (2006.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/084 (2023.01); G06N 3/088 (2023.01); G06N 5/046 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); H04B 17/309 (2015.01); H04B 17/318 (2015.01); H04L 1/00 (2006.01); H04L 1/18 (2023.01); H04L 67/1097 (2022.01); H04L 67/12 (2022.01); G06F 16/23 (2019.01); G06F 16/245 (2019.01); G06N 3/126 (2023.01); H04L 5/00 (2006.01);
U.S. Cl.
CPC ...
G05B 23/0283 (2013.01); G01M 13/028 (2013.01); G01M 13/045 (2013.01); G05B 13/028 (2013.01); G05B 19/4183 (2013.01); G05B 19/4184 (2013.01); G05B 19/41845 (2013.01); G05B 19/4185 (2013.01); G05B 19/41865 (2013.01); G05B 19/41875 (2013.01); G05B 23/0221 (2013.01); G05B 23/0229 (2013.01); G05B 23/024 (2013.01); G05B 23/0264 (2013.01); G05B 23/0286 (2013.01); G05B 23/0289 (2013.01); G05B 23/0291 (2013.01); G05B 23/0294 (2013.01); G05B 23/0297 (2013.01); G06F 16/24 (2019.01); G06F 18/2178 (2023.01); G06N 3/006 (2013.01); G06N 3/02 (2013.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/084 (2013.01); G06N 3/088 (2013.01); G06N 5/046 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/0004 (2013.01); H04B 17/309 (2015.01); H04B 17/318 (2015.01); H04L 1/0002 (2013.01); H04L 1/0041 (2013.01); H04L 1/18 (2013.01); H04L 67/1097 (2013.01); H04L 67/12 (2013.01); G05B 2219/32287 (2013.01); G05B 2219/35001 (2013.01); G05B 2219/37337 (2013.01); G05B 2219/37351 (2013.01); G05B 2219/37434 (2013.01); G05B 2219/40115 (2013.01); G05B 2219/45004 (2013.01); G05B 2219/45129 (2013.01); G06F 16/2379 (2019.01); G06F 16/245 (2019.01); G06N 3/126 (2013.01); H04L 5/0064 (2013.01); Y02P 80/10 (2015.11); Y02P 90/02 (2015.11);
Abstract

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may perform a method of predicting a service event from vibration data captured data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine can be generated based on a severity unit calculated for the captured vibration.


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