Bayesian Prognostic Failure Model for ASoSC2 using a model-of-models approach

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$70,000.00
Award Year:
2008
Program:
SBIR
Phase:
Phase I
Contract:
W31P4Q-09-C-0059
Award Id:
87126
Agency Tracking Number:
A082-039-0838
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1235 South Clark Street, Suite 400, Arlington, VA, 22202
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
036593457
Principal Investigator:
Dan Schrimpsher
Senior Engineer
(256) 895-4331
dan.schrimpsher@dac.us
Business Contact:
Kelly McClelland
Director, Corporate Business Office
(703) 414-5024
kelly.mcclelland@dac.us
Research Institution:
n/a
Abstract
We propose a "model-of-models" approach for building a Bayesian Prognostic Failure Model that will meet Army IAMD requirements for the ASoSC2. The model-of-models approach closely parallels the system-of-systems approach pursued by the Army for fielding its weapon, sensor, and C2 systems. It is highly modular and will allow warfighters in the field to easily reconfigure the prognostic tool when "plug and fight" hardware is reconfigured on the battlefield. Our approach models mission critical failures by capturing the (possibly many) way that individual component failures can contribute to a system failure. Our models will exploit component reliability data already available and provides an organized and mathematically principled approach to combining that data. Our team consists of staff who have already contributed to the development and testing of candidate components for the ASoSC2 system as well as mathematicians and computer scientist with extensive experience in Bayesian modeling and reasoning. In addition, Decisive Analytics has a long history of transitioning SBIR technologies to end-users and will work with the prime contractor to integrate the Bayesian Prognostic Failure Model into the deployed ASoSC2.

* information listed above is at the time of submission.

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