Abstract
The development of wireless structural health monitoring systems utilising low-cost MEMS sensors, promises to make the deployment of large scale sensor networks for damage detection economically feasible. However, wireless transmission of full vibration time series is a potentially expensive and difficult bottleneck in the monitoring process. Smart sensors, equipped with microprocessors for computation, allow damage detection algorithms to be executed prior to the wireless transmission of data. This decentralised, parallel computing approach can condense the amount of data to be transmitted, potentially reducing power consumption and increasing transmission reliability. In this paper, several data-based damage detection algorithms are implemented on board a custom built MEMS sensor mote equipped with a microcontroller. A real campus building is instrumented with these sensors, and a quantitative study of the tradeoff between computation and transmission is conducted to investigate the advantages of carrying out part of the damage detection process at the sensor level.
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