Dynamically Reconfigurable Data Architectures for Aircraft Data Analysis and Anomaly Detection

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$740,317.00
Award Year:
2010
Program:
SBIR
Phase:
Phase II
Contract:
N00014-10-C-0255
Award Id:
82712
Agency Tracking Number:
N071-084-0655
Solicitation Year:
2007
Solicitation Topic Code:
N07-084
Solicitation Number:
2007.1
Small Business Information
550 Paiea Street, Suite #236, Honolulu, HI, -
Hubzone Owned:
N
Minority Owned:
Y
Woman Owned:
N
Duns:
176527471
Principal Investigator:
Matt Shawver
Computer Scientist
(808) 423-1900
mshawver@referentia.com
Business Contact:
Nelson Kanemoto
President&CEO
(808) 423-1900
nkanemoto@referentia.com
Research Institute:
n/a
Abstract
State-of-the-art software tools for the storage, exploration, search, and analysis of large volumes of time series data such as that collected from fleet-wide Aircraft Condition Monitoring Systems (ACMS) are limited. ACMS and maintenance data are not utilized to their full potential to provide for accelerated diagnostic support; acquisition and maintenance of systems engineering knowledge; development of prognostic and diagnostic algorithms; and validation of prognostic and diagnostic algorithms. To address these deficiencies, we will enhance our Time Series: Rapid Exploration (T-REX) software system to provide data management, data visualization, data analysis, and algorithm development and validation for fleet-wide ACMS and maintenance data. The ability to more effectively manage the data, place it in the context of maintenance actions, and analyze the data using engineering analysis tools will enable subject matter experts to gain insight into the health of their aircraft fleet. The T-REX system supports storage and analysis of very large sets of disparate source data. T-REX enables analysts to interactively exploit terabytes of time-synchronized data to identify spatial and temporal relationships among the data sets. Once the relationships have been defined, T-REX validates them and correlates the data with historical information.

* information listed above is at the time of submission.

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