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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