Advance Failure Warning via Data Driven Stochastic Models

Award Information
Agency:
Department of Defense
Branch
Missile Defense Agency
Amount:
$69,998.00
Award Year:
2003
Program:
STTR
Phase:
Phase I
Contract:
N6554003C0061
Award Id:
64431
Agency Tracking Number:
03-0018T
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
Barron Assoc., Inc. (Currently BARRON ASSOCIATES, INC.)
1160 Pepsi Place, Suite 300, Charlottesville, VA, 22901
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
120839477
Principal Investigator:
B. Eugene Parker, Jr.
Senior Research Scientist
(434) 973-1215
parker@barron-associates.com
Business Contact:
David Ward
Chairman
(434) 973-1215
barron@barron-associates.com
Research Institution:
PRINCETON UNIV.
Michelle D Christy
4 New South Building
Princeton, NJ, 08544
(609) 258-7508
Nonprofit college or university
Abstract
The ideal prognostic system would be generic, requiring no domain-specific knowledge for its application. The prognostic system must also perform across all operating regimes without generating an excessive number of false alarms, and must successfullydiagnose problems. The semi-empirical technique proposed herein by Barron Associates, Inc. and its academic partners at Princeton University takes advantage of sophisticated black-box modeling, efficient nonparametric state estimation, and sensitivestatistical change detection and isolation algorithms. Observers composed of banks of unscented particle filters will be used to estimate the internal states of the system that is being monitored, after a model has been constructed from preliminary data.These resulting state estimates will then be used to synthesize output signals that are insensitive to noise and unmodelled dynamics, but sensitive to faults. A statistical change detection technique (based on a modification of the standard generalizedlikelihood ratio statistic) is then used to process these output signals to detect faults in real time. Detection of anomalous behavior is made subject to user-specified probabilities for false alarms and missed detections. Analytical redundancy andprobabilistic modelling will be used to ensure the correct diagnosis and isolation of problems. The ability to predict machine/equipment events has significant commercial potential in aircraft, power, manufacturing, processing, transportation, and otherindustrial applications where such capability would allow companies to improve reliability and safety, reduce downtime, and lower the direct maintenance cost of physical assets.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government