Abstract: Structural health monitoring methods based on modal properties have
been proven to be well suited for infrastructure objects, e.g. bridges,
buildings or wind turbines. The considerable size of these structures leads to
long distances between the sensors and the signal processing units and a large
number of sensors. To save cabling effort and lower the amount of data which
has to be transmitted, the structural analysis may be decentralized with a
network of smart sensors. In this paper, a strategy for decentralized signal
analysis with the Random Decrement method is discussed. This method allows for
the estimation of auto- and cross-correlation functions simply from triggered
averaging of time histories. These serve as a basis for the modal decomposition
by operational modal analysis methods. While the Random Decrement signatures
can be processed in a decentralized way on the small microcontrollers commonly
used, e.g. in wireless sensing applications, the deeper analysis of resonance
frequencies and mode shapes is done by a central unit. After a short introduction into the theory of the Random Decrement method, its application in
decentralized data acquisition is illustrated with a numerical example.
Afterwards, a simple experimental structure exposed to actual wind loads is
instrumented and the decentralized signal processing strategy is tested with
the acquired data. A first algorithm for the modal decomposition of the
estimated correlation functions is implemented and tested.
Keywords: Modal analysis, modal testing, structural health monitoring
