Intelligent Sensors with Application to the Identification of Structural Modal Parameters and Steel Cable Forces

Wireless sensing systems have been proposed for structural heath monitoring in recent years. While wireless sensors are cost-competitive compared to tethered monitoring systems, their real merit lies in their embedded computational capabilities. The academic wireless sensing prototype co-developed at Stanford University and the University of Michigan is characterized by a powerful computational core and low power consumption characteristics. In this paper, two embedded engineering algorithms, namely the fast Fourier transform and peak-picking algorithm, are implemented in the wireless sensing and validated by laboratory and field experimental studies. Moreover, the wireless sensors are applied to the problem of identifying structural modal parameters and forces in bridge steel cables. Identification results derived from the intelligent wireless sensor using the computing core are compared with those obtained by off-line engineering analysis of the measured time-history data. Such a comparison serves to validate the effectiveness of the powerful computational core of the intelligent wireless sensor. It is shown that self-interrogation of measurement data using the two embedded algorithms greatly reduces the amount of data to be transmitted by the wireless sensing network. Thus, the wireless sensor offers scalable network solutions that are power-efficient.