Heuristic-based Prognostic and Diagnostic Methods to Enhance Intelligent Power Management for Tactical Electric Power Generator Sets

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$70,000.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
W15P7T-11-C-H212
Award Id:
97924
Agency Tracking Number:
A102-105-0232
Solicitation Year:
n/a
Solicitation Topic Code:
ARMY 10-105
Solicitation Number:
n/a
Small Business Information
200 Greenleaf St., Fort Worth, TX, 76107
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
008038846
Principal Investigator:
Chris Stimek
Staff Electrical Engineer
(817) 872-1500
chris.stimek@williams-pyro.com
Business Contact:
Brent Williams
Chief Operations Officer
(817) 872-1500
brent.williams@williams-pyro.com
Research Institute:
n/a
Abstract
Current Joint Operations in the Middle East have highlighted the need for increased system reliability and reduced petroleum consumption as both a cost reduction and force protection mechanism in the tactical battlefield. Williams-Pyro, Inc., is proposing to develop the Generator Fault Investigation Technology (GenFIT) system to perform diagnostics and prognostics on diesel generators with the end result of helping the Army achieve its HI-Power goals through reducing generator down time, improving fuel efficiency, and reducing emissions. The GenFIT system will be able to easily integrate with deployed TQGs to provide diagnostic and prognostic information to maintenance personnel, reducing the time required to service these generators. Many existing condition-based maintenance (CBM) systems are extremely complex, relying on neural networks and pattern recognition algorithms that need large amounts of equipment-specific training data. In contrast, Williams-Pyro is proposing to use a top-down approach to develop a first order diagnostics and prognostics methodology based on heuristic models derived from an understanding of diesel generator operating principals, observed generator performance values, and known generator parameters. The technology developed will be able to identify long-term generator performance degradation as well as reduce fuel consumption and emissions through proper maintenance and operation of the generator.

* information listed above is at the time of submission.

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