Advanced Data Processing, Storage and Visualization Algorithms for Structural Health Monitoring Sensor Networks of Naval Assets Navy STTR FY2010.A Sol No.: Navy STTR FY2010.A Topic No.: N10A-T042 Topic Title: Advanced Data Processing, Storage and Visualization Algorithms for Structural Health Monitoring Sensor Networks of Naval Assets Proposal No.: N10A-042-0385 Firm: Acellent Technologies, Inc. 835 Stewart Drive Sunnyvale, California 94085-4514 Contact: X. Qing Phone: (408) 745-1188 Web Site: www.acellent.com 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. Benefits: The proposed development includes developing non-linear models to identify the material state of structures and the precursors to the onset of damage. Current SHM techniques are non-existent for damage precursors. In the case of damage detection, current inspection methods are labor-intensive and time-consuming and as such less reliable and expensive. The proposed system offers an innovative alternative that provides many advantages over the conventional techniques. Once developed and installed, the SHM system can be applied at any time to automatically monitor the material state of the structures. The system is cost-effective and more reliable because of minimal human involvement. By detecting precursors to damages, it will provide an early warning to users about the onset of damage, thus providing more time and options to identify the most cost-effective repair and maintenance solution. It will also enable reducing the high margins of safety that is currently being used in the design of these structures. The ability to detect precursors to damages will be of interest in a wide range of markets. The technology can be applied in the aircraft, rotorcraft, marine and offshore platform industries to name a few. It can be used for the maintenance of existing structures as well as in the design of new platforms to reduce the high margins of safety. The system will provide the structure owners and maintenance personnel with real-time reliable information about the condition of the structures while in service. Such knowledge will lead to increased safety of operation, improved reliability of the structures and reduced maintenance cost through system automation. Return