Automated Knowledge Discovery and Reliability Analysis for the F414 Engine

Award Information
Agency:
Department of Defense
Branch:
Navy
Amount:
$148,987.00
Award Year:
2008
Program:
SBIR
Phase:
Phase I
Contract:
N68335-08-C-0266
Agency Tracking Number:
N081-038-0439
Solicitation Year:
2008
Solicitation Topic Code:
N08-038
Solicitation Number:
2008.1
Small Business Information
IMPACT TECHNOLOGIES, LLC
200 Canal View Blvd, Rochester, NY, 14623
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
073955507
Principal Investigator
 G. Scott Valentine
 Mgr., Logistics & Maintenance Sys.
 (585) 424-1990
 scott.valentine@impact-tek.com
Business Contact
 Mark Redding
Title: President
Phone: (585) 424-1990
Email: mark.redding@impact-tek.com
Research Institution
N/A
Abstract
Impact Technologies, LLC, with support from General Electric, proposes to develop and demonstrate an automated knowledge discovery and reliability analysis tool for the F414 engine. The proposed work is fully based on the F414 maintenance data available in the Maintenance Data Warehouse (MDW) maintained by GE. Knowledge discovery techniques will be applied to unit, intermediate and depot level maintenance records obtained from the MDW. Using that knowledge, innovative reliability analysis techniques that are well suited to addressing the competing risk problem, as components are maintained before they fail, will be implemented in this effort. Once fleet-wide in-service component reliability is calculated, the reliability of each individual component will be tracked. Significant deviations in reliability will be detected early on and will be signaled to the user. An opportunistic maintenance optimization module will also use in-service reliability to provide decision support on what maintenance is warranted. Specifically the core innovations of the proposed work include: 1) a knowledge discovery module that processes maintenance records; 2) an in-service component reliability analysis module that addresses the competing risks problem; 3) development of a component reliability tracking module; and 4) an opportunistic maintenance optimization module that would maximize the expected time on wing.

* information listed above is at the time of submission.

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