Shock and Vibration

Shock and Vibration / 2010 / Article
Special Issue

International Conference on Structural Engineering Dynamics – ICEDyn 2009

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Volume 17 |Article ID 327426 |

A. Friedmann, D. Mayer, M. Kauba, "An Approach for Decentralized Mode Estimation Based on the Random Decrement Method", Shock and Vibration, vol. 17, Article ID 327426, 10 pages, 2010.

An Approach for Decentralized Mode Estimation Based on the Random Decrement Method

Received18 Jun 2010
Accepted18 Jun 2010


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.

Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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