Cognitive Technology for Advanced Maintenance

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,704.00
Award Year:
2006
Program:
SBIR
Phase:
Phase I
Contract:
M67854-07-C-6502
Award Id:
76968
Agency Tracking Number:
N062-108-0637
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
850 Energy Drive, Idaho Falls, ID, 83401
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
089822014
Principal Investigator:
Sean Marble
President
(802) 876-3100
smarble@sentientscience.com
Business Contact:
Sean Marble
President
(802) 876-3100
smarble@sentientscience.com
Research Institution:
n/a
Abstract
Although prognostic health monitoring systems for the mechanical aspects of the LAV-25 are currently under development, a comparable system for diagnosis of the electrical and weapons systems does not exist. System diagnosis is an expertise-based craft that is easily lost when turnover rates (due to promotion or personnel leaving the Corps) are rapid. Built in test (BIT) and built in test equipment (BITE) provide symptoms to assist the diagnosis, but they do not fully confirm where suspected problems reside. The complexity of indications, the potential number of indicators, and the many to one ratio of faults to indicators overwhelm maintainers trying to diagnose system faults. Factors such as usage, environment, and recent maintenance affect the way symptoms present. Maintainers require intelligent diagnostic systems that can recommend paths forward when maintainers are faced with complex symptoms. Sentient Corporation will leverage its expertise in system prognostics and automated reasoners to develop an intuitive Automated Intelligent Maintainer Support (AIMS) system with a light-weight, portable advanced user interface. AIMS will combine information derived from TETS with maintenance data, expert knowledge, BIT data, and FMECA, and will act as an assistant in the diagnosis and repair of problems in the on-board weapons systems of the LAV-25. AIMS will be adjustable for user experience level, self-learning to rapidly and fully leverage accumulated experience across the fleet, and flexible enough to be adapted to other weapons platforms in the future.

* information listed above is at the time of submission.

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