Structural health monitoring from discrete binary data through pattern recognition

Abstract eng:
A continuing challenge in structural health monitoring is power availability for sensors to collect and communicate data. While self-powered sensors are helping address this concern, the harvested power with current technology is limited and improving the network efficiency requires reducing the power budget. A way to minimize the communication power demand is to transmit the minimum amount of information, namely one bit. The binary signal can be generated at a sensor node according to a local rule based on physical measurements, but interpretation at the global level requires dealing with discrete binary (1 or 0) data. This study presents an investigation on Pattern Recognition (PR) methods adapted from image data analysis techniques for the interpretation of binary data for use in structural health monitoring. The ability of the PR methods to identify service demands and localized material degradation was evaluated through finite element simulations and experiments on simple plates. Results indicates that PR techniques are able to use binary data to discern structural response and detect the presence and location of damage.

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