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Meta-Data Mining for Optimized Aircraft Repair and Overhaul

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8501-09-P-0142
Agency Tracking Number: F083-243-0270
Amount: $99,839.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: AF083-243
Solicitation Number: 2008.3
Timeline
Solicitation Year: 2008
Award Year: 2009
Award Start Date (Proposal Award Date): 2009-05-01
Award End Date (Contract End Date): 2010-01-27
Small Business Information
811 Court St.
Utica, NY 13502
United States
DUNS: 124152138
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Seymour Morris
 Senior Reliability Engine
 (315) 351-4211
 smorris@quanterion.com
Business Contact
 Preston MacDiarmid
Title: President
Phone: (315) 732-0097
Email: pmacdiarmid@quanterion.com
Research Institution
N/A
Abstract

This research effort seeks to develop more accurate aircraft maintenance requirements forecasts and repair package optimization techniques.  The capability to forecast using meta-data generation and data mining techniques applied to the Air Force REMIS maintenance data collection system, along with data fusion from other sources, such as prognostics data, is evaluated.  The feasibility of developing more meaningful and information rich meta-data from raw REMIS data is investigated along with data mining techniques and tools for applying time series statistical analysis techniques for identifying patterns and trends, either from raw REMIS data or from developed meta-data.  The application of specific techniques such as data clustering, association, regression analysis, autocorrelation and data visualization are evaluated for their application to REMIS data using a variety of readily available open-source software packages geared toward these types of problems.  The feasibility of leveraging and customizing selected open-source data mining related software solutions are evaluated for application to the aircraft maintenance forecasting problem and future commercialization. BENEFIT: The anticipated benefit of this research is more efficient aircraft depot maintenance planning, which can also be expected to lead to higher aircraft mission readiness/availability and lower life cycle cost.  This is expected to occur through improved meta-data design, data fusion, data mining and time series trending analysis techniques. Potential commercial applications include many organizations that maintain fleets of vehicles (aircraft, cars, trucks, etc.), which have raw historical maintenance data that can be analyzed to identify patterns and trends, leading to more optimized maintenance planning and execution

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

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