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Automated Knowledge Discovery and Reliability Analysis for the F414 Engine

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-10-C-0113
Agency Tracking Number: N081-038-0439
Amount: $899,548.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: N08-038
Solicitation Number: 2008.1
Solicitation Year: 2008
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-08-09
Award End Date (Contract End Date): 2012-08-09
Small Business Information
200 Canal View Blvd
Rochester, NY 14623
United States
DUNS: 073955507
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Scott Valentine
 Manager, Maint. & Logisti
 (585) 424-1990
Business Contact
 Mark Redding
Title: President
Phone: (585) 424-1990
Research Institution

Impact Technologies, LLC proposes to further develop and demonstrate an automated knowledge discovery and reliability analysis tool for the F414 engine. The work is based on available data in NAVY databases such as DECKPLATE or DECKETR or potentially the Maintenance Data Warehouse (MDW) maintained by GE. Knowledge discovery techniques will be applied to maintenance records. Using that knowledge, innovative reliability analysis techniques will calculate component reliabilities using tracked life usage indicators and maintenance information. These techniques are well suited to addressing the competing risk problem as we deal with multiple failure modes and the right-censoring problem, as components are maintained before they fail. Once fleet-wide in-service component reliability is calculated, it will be trended and tracked. Users will be notified of any significant deviations in reliability early on. An opportunistic maintenance/component matching optimization module will also use in-service reliability to make recommendations on warranted maintenance and component replacement selection. Specifically the core innovations of the proposed work include: 1) a knowledge discovery module that processes maintenance records, trends reliability and detects outliers; 2) an in-service component reliability analysis module based on historical maintenance records; and 3) an opportunistic maintenance/component matching optimization module that would maximize the expected time on wing.

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

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