Advanced Data Processing, Storage and Visualization Algorithms for Structural Health Monitoring Sensor Networks of Naval Assets

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$70,000.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N00014-10-M-0311
Award Id:
95182
Agency Tracking Number:
N10A-042-0385
Solicitation Year:
n/a
Solicitation Topic Code:
NAVY 10T042
Solicitation Number:
n/a
Small Business Information
835 Stewart Drive, Sunnyvale, CA, 94085
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
043688410
Principal Investigator:
X. Qing
Director of Sensor Techno
(408) 745-1188
peterq@acellent.com
Business Contact:
Vindhya Narayanan
VP Business
(408) 745-1188
vindhya@acellent.com
Research Institute:
North Carolina State University
F. G Yuan
Department of Mech and Aero
1009 Capability Dr
Raleigh, NC, 27695
(919) 515-5947
Nonprofit college or university
Abstract
Acellent Technologies Inc. and Prof. F. G. Yuan at North Carolina State University (NCSU) are proposing to develop a Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to determine materials state awareness and its evolution, including identification of precursors, detection of microdamages and flaws near high stress area or in a distributed region. The SMART Layer concept will be used as a basis for the development of the hybrid distributed sensor network. The nonlinear behavior of microstructure defects (called micro-defects hereafter), which is intentionally eliminated or simply disregarded in the current conventional ultrasonic diagnosis, will be served as the basis for the development of nonlinear diagnostics for materials state awareness. The Self-learning Symbiotic Diagnostic Algorithms will employ nonlinear acoustic interpretation and statistical data driven analysis. The approach will be based on the principal physics of nonlinearity of materials and its effect on macro scale sensor signals together with an intelligent self instructing data driven algorithm as a wrapper program. Once developed, the sensor network permanently integrated with the structure can be used to accurately and robustly detect the precursors to damages that occur in the structure during scheduled stops or during scheduled maintenance intervals.

* information listed above is at the time of submission.

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