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A Testbed for Data Fusion for Gas Turbine Engine Diagnostics and Predictive Diagnostics

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: F33615-01-C-2129
Agency Tracking Number: 001PR-2320
Amount: $0.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Solicitation Year: N/A
Award Year: 2001
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
10299 Scripps Trail, PMB 231
San Diego, CA 92131
United States
DUNS: 097858836
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Thomas Brotherton
 VP, Chief Technical Offic
 (858) 679-4140
Business Contact
 Laura Goodrich
Title: Vice President, Finance
Phone: (858) 679-4140
Research Institution

The key to producing more confident, real-time, on-wing diagnoses resides in the processing of multisensor data. Adding expensive new sensors is not the solution, but data fusion is. Fusion saves cost and weight; no new sensors are required. Fusionreduces false alarm rates; faults to be seen across multiple sensors. Diagnostic performance is improved by allowing detection of unique fault patterns seen on sets of sensors instead of a single sensor. Fusion enables predictive diagnostics; low-levelinformation is integrated across sensors so potential faults can be detected earlier. In Phase IIAC developed a PC-based gas turbine engine health monitoring system that uses a combination of signal and information processing algorithms to perform data fusion for engine fault diagnostics and predictive diagnostics. The system is built around anIntelligent Mechanical Diagnostics System (i-mds) Matlab toolbox that aids in systems development of future and in the integration of new sensors and new information. In Phase II that fusion technology and i-mds will be further developed and included intoan integrated system to support individual aircraft field maintenance, fleet maintenance, as well as research engineers in performing diagnostics and predictive diagnostics on real data. That development includes significant amounts of real data.

* Information listed above is at the time of submission. *

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