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

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0311
Agency Tracking Number: N10A-042-0385
Amount: $70,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N10A-T042
Solicitation Number: 2010.A
Solicitation Year: 2010
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-28
Award End Date (Contract End Date): 2011-04-30
Small Business Information
835 Stewart Drive
Sunnyvale, CA 94085
United States
DUNS: 043688410
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 X. Qing
 Director of Sensor Techno
 (408) 745-1188
Business Contact
 Vindhya Narayanan
Title: VP Business
Phone: (408) 745-1188
Research Institution
 North Carolina State University
 F. G Yuan
Department of Mech and Aero 1009 Capability Dr
Raleigh, NC 27695
United States

 (919) 515-5947
 Nonprofit College or University

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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