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
Contract: W15P7T-11-C-H212
Agency Tracking Number: A102-105-0232
Amount: $120,000.00
Phase: Phase I
Program: SBIR
Awards Year: 2010
Solicitation Year: 2010
Solicitation Topic Code: A10-105
Solicitation Number: 2010.2
Small Business Information
200 Greenleaf St., Fort Worth, TX, 76107
DUNS: 008038846
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Chris Stimek
 Staff Electrical Engineer
 (817) 872-1500
Business Contact
 Brent Williams
Title: Chief Operations Officer
Phone: (817) 872-1500
Email: brent.williams@williams-pyro.com
Research Institution
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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