The Adverse Event Detection (AED) system described in this paper supports nondestructive evaluation (NDE) systems and evaluates advanced composite structures such as wind turbine blades. AED is a joint effort by Extreme Diagnostics and Virginia Polytechnic Institute and State University (Virginia Tech). AED uses the impedance method to monitor bulk structural integrity, wave propagation methods to assess surfaces, and acoustic emission (AE) structural health monitoring (SHM) to detect adverse events such as impacts. The incorporation of AE methods significantly increases the sensor coverage area, which is crucial in health monitoring of large-scale structures like wind turbine blades. Our AED system provides on-line assessment of structural integrity during normal operations, as opposed to traditional nondestructive evaluation (NDE) methods that are commonly applied off-line—that is, systems must be shut down for inspection by conventional NDE. Our AED system not only provides timely information during operation, it can also reveal defects that only become apparent under operational stress—such defects can be overlooked by traditional off-line inspection methods. AED actively examines structures across several length and time scales in an autonomous fashion, thus greatly reducing the number of sensors required and lowering system complexity and cost. Our early AED prototype demonstrated impedance-based SHM in wind turbine blades. This project integrates three previously independent SHM approaches, and demonstrates damage detection on a composite structure. Our current AED prototype is a low-power, wireless, and embeddable sensor that detects incipient damage in near real-time and automatically provides early warning of structural damage. Our AED system is also suited to a variety of aerospace applications that include composite overwrapped pressure vessels. AED also supports Homeland Security, and furthers national preparedness by monitoring infrastructure integrity and disaster response by providing damage assessment.
How to Cite
S. Deyerle, D. S. Ha and D. J. Inman (2010). “A low-power system design for Lamb wave methods,” SPIE Smart Structures/NDE 2010, 7-11 March, San Diego CA, 2010.
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D. Zhou, N. Kong, D. S. Ha and D. J. Inman (2010). “A self-powered wireless SHM sensor node,” SPIE Smart Structures/NDE 2010, 7-11 March, San Diego CA, 2010.
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