Self-Evolving Maintenance Knowledge Bases

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,995.00
Award Year:
2004
Program:
SBIR
Phase:
Phase I
Contract:
N68335-04-C-0178
Award Id:
69921
Agency Tracking Number:
N041-010-1762
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
75 5th Street NW Suite 312, Atlanta, GA, 30308
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
076228811
Principal Investigator:
Irtaza Barlas
Vice President Software D
(404) 526-6188
irtaza.barlas@iasatl.com
Business Contact:
J. Dorrity
President
(404) 526-6188
lew.dorrity@iasatl.com
Research Institution:
n/a
Abstract
Intelligent Automation Systems, in collaboration with the Georgia Institute of Technology proposes the development of Self-Evolving Knowledge Base architecture. Specifically, this project will consist of 1) Analysis to identify maintenance related issues such as failure modes, criticality, etc for a selected test bed that will comprise of a critical subsystem or component; 2) Systematic knowledge engineering steps to characterize the maintenance, logistics, and inventory related issues, and to elicit domain as well as control knowledge; 3) Development of a novel architecture and algorithms for the integration of the control knowledge of self-evolution for a continuous improvement process in a feedback manner; 4) Development of testing plan to validate and verify the self-evolving architecture on a subsystem or component; and 5) Software demonstration of the framework. This proposal involves a novel artificial intelligence technique of reasoning and learning in episodic manner. This methodology forms the core of what is presented as a comprehensive CATER (CAse-based TEmporal Reasning) solution that will exhibit self-evolution process by improving the performance of knowledge-base by observing the feedback generated from multiple sources, such as maintenance logs, IETMs, inventory footprint, component statistics, etc. The solution has a built in capacity to reduce the uncertainty that is inherent in the PHM and maintenance processes.

* information listed above is at the time of submission.

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