USA flag logo/image

An Official Website of the United States Government

Advance Failure Warning via Data Driven Stochastic Models

Award Information

Agency:
Department of Defense
Branch:
Missile Defense Agency
Award ID:
64431
Program Year/Program:
2003 / STTR
Agency Tracking Number:
03-0018T
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Barron Associates, Inc.
1410 Sachem Place Suite 202 Charlottesville, VA 22901-2496
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 2003
Title: Advance Failure Warning via Data Driven Stochastic Models
Agency / Branch: DOD / MDA
Contract: N6554003C0061
Award Amount: $69,998.00
 

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.

Principal Investigator:

B. Eugene Parker, Jr.
Senior Research Scientist
4349731215
parker@barron-associates.com

Business Contact:

David G. Ward
Chairman
4349731215
barron@barron-associates.com
Small Business Information at Submission:

Barron Assoc., Inc.
1160 Pepsi Place, Suite 300 Charlottesville, VA 22901

EIN/Tax ID: 541243694
DUNS: N/A
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Research Institution Information:
PRINCETON UNIV.
4 New South Building
Princeton, NJ 08544
Contact: Michelle D. Christy
Contact Phone: (609) 258-7508
RI Type: Nonprofit college or university