Advance Failure Warning via Data Driven Stochastic Models

Award Information
Agency: Department of Defense
Branch: Missile Defense Agency
Contract: N6554003C0061
Agency Tracking Number: 03-0018T
Amount: $69,998.00
Phase: Phase I
Program: STTR
Awards Year: 2003
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
1160 Pepsi Place, Suite 300, Charlottesville, VA, 22901
DUNS: 120839477
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 B. Eugene Parker, Jr.
 Senior Research Scientist
 (434) 973-1215
 parker@barron-associates.com
Business Contact
 David Ward
Title: Chairman
Phone: (434) 973-1215
Email: 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. *

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