Optimal sensor placement for multi-setup modal analysis of structures

Abstract Modal tests on large structures are often performed in multiple setups for practical reasons. Several sensors are kept fixed as reference sensors over all setups, while the other, so called roving sensors, are moved from one setup to another. This paper develops an optimal sensor placement strategy for multi-setup modal identification, which simultaneously optimizes the locations of the reference sensors and roving sensors. As an optimality criterion, the Information Entropy is adopted, which is a scalar measure of uncertainty in the Bayesian framework. The focus in the application goes to repetitive structures where modes typically occur in clusters, with closely spaced natural frequencies and similar wavelengths. The proposed strategy is illustrated for selecting optimal positions of uni-axial sensors for a repetitive frame structure. The influence of the number of reference sensors and two strategies for positioning roving sensors, i.e. a cluster and a uniform distribution of roving sensors, are investigated. The number of reference sensors is found to be preferably equal to or larger than the number of modes to be identified. In this case, the information content, as quantified by the Information Entropy, is not very sensitive to the roving sensor strategy. If less reference sensors are used, it is highly preferred to distribute the roving sensors uniformly over the structure instead of clustering them. The proposed strategy has been validated by an experimental modal test on a floor of an office building of KU Leuven, which has a nearly repetitive structural layout. The results show how optimally locating sensors allows extracting more information from the data. Though the focus is on applications involving repetitive structures, the proposed strategy can be applied to multi-setup modal identification of any large structure.