Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information
Abstract
Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.
- Inventors:
- Issue Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1158924
- Patent Number(s):
- 8838519
- Application Number:
- 13/646,081
- Assignee:
- UT-Battelle, LLC (Oak Ridge, TN)
- Patent Classifications (CPCs):
-
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G - PHYSICS G06 - COMPUTING G06F - ELECTRIC DIGITAL DATA PROCESSING
- DOE Contract Number:
- AC05-00OR22725
- Resource Type:
- Patent
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Hively, Lee M. Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information. United States: N. p., 2014.
Web.
Hively, Lee M. Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information. United States.
Hively, Lee M. Tue .
"Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information". United States. https://www.osti.gov/servlets/purl/1158924.
@article{osti_1158924,
title = {Graph-theoretic analysis of discrete-phase-space states for condition change detection and quantification of information},
author = {Hively, Lee M.},
abstractNote = {Data collected from devices and human condition may be used to forewarn of critical events such as machine/structural failure or events from brain/heart wave data stroke. By monitoring the data, and determining what values are indicative of a failure forewarning, one can provide adequate notice of the impending failure in order to take preventive measures. This disclosure teaches a computer-based method to convert dynamical numeric data representing physical objects (unstructured data) into discrete-phase-space states, and hence into a graph (structured data) for extraction of condition change.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {9}
}
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