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Title: Adaptive model training system and method

Abstract

An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.

Inventors:
;
Issue Date:
Research Org.:
Intellectual Assets LLC, Lake Tahoe, NV (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1163977
Patent Number(s):
8,892,478
Application Number:
13/803,436
Assignee:
Intellectual Assets LLC (Lake Tahoe, NV)
DOE Contract Number:  
FG02-04ER83949
Resource Type:
Patent
Resource Relation:
Patent File Date: 2013 Mar 14
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Bickford, Randall L, and Palnitkar, Rahul M. Adaptive model training system and method. United States: N. p., 2014. Web.
Bickford, Randall L, & Palnitkar, Rahul M. Adaptive model training system and method. United States.
Bickford, Randall L, and Palnitkar, Rahul M. Tue . "Adaptive model training system and method". United States. https://www.osti.gov/servlets/purl/1163977.
@article{osti_1163977,
title = {Adaptive model training system and method},
author = {Bickford, Randall L and Palnitkar, Rahul M},
abstractNote = {An adaptive model training system and method for filtering asset operating data values acquired from a monitored asset for selectively choosing asset operating data values that meet at least one predefined criterion of good data quality while rejecting asset operating data values that fail to meet at least the one predefined criterion of good data quality; and recalibrating a previously trained or calibrated model having a learned scope of normal operation of the asset by utilizing the asset operating data values that meet at least the one predefined criterion of good data quality for adjusting the learned scope of normal operation of the asset for defining a recalibrated model having the adjusted learned scope of normal operation of the asset.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {11}
}

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Real-time sensor validation for propulsion systems
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