Data Mining for IVHM using Sparse Binary Ensembles

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$99,744.00
Award Year:
2008
Program:
SBIR
Phase:
Phase I
Contract:
NNX08CA53P
Award Id:
87904
Agency Tracking Number:
074652
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
1777 Highland Drive, Suite B, Ann Arbor, MI, 48108
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
969868298
Principal Investigator:
John Trenkle
Principal Investigator
(734) 975-8777
jtrenkle@michiganaerospace.com
Business Contact:
Peter Tchoryk
Chief Executive Officer
(734) 975-8777
ptchoryk@michiganaerospace.com
Research Institution:
n/a
Abstract
In response to NASA SBIR topic A1.05, "Data Mining for Integrated Vehicle Health Management", Michigan Aerospace Corporation (MAC) asserts that our unique SPADE (Sparse Processing Applied to Data Exploitation) technology meets a significant fraction of the stated criteria and has functionality that enables it to handle many applications within the aircraft lifecycle. SPADE distills input data into highly quantized features and uses MAC's novel techniques for constructing Ensembles of Decision Trees to develop extremely accurate diagnostic/prognostic models for classification, regression, clustering, anomaly detection and semi-supervised learning tasks. These techniques are currently being employed to do Threat Assessment for satellites in conjunction with researchers at the Air Force Research Lab. Significant advantages to this approach include: 1) completely data driven; 2) training and evaluation are faster than conventional methods; 3) operates effectively on huge datasets (> billion samples X > million features), 4) proven to be as accurate as state-of-the-art techniques in many significant real-world applications. The specific goals for Phase 1 will be to work with domain experts at NASA and with our partners Boeing, SpaceX and GMV Space Systems to delineate a subset of problems that are particularly well-suited to this approach and to determine requirements for deploying algorithms on platforms of opportunity.

* information listed above is at the time of submission.

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