This tutorial paper aims to introduce the topic of operational modal analysis to non-specialists on the subject. First of all, it is stressed the relevance of this experimental technique particularly in the assessment of important civil infrastructure. Then, after a synthesis of required theoretical background, three of the most powerful algorithms for output-only modal identification are presented. The several steps of these identification procedures are illustrated with the processing of data collected on a concrete arch bridge with a span of 280 m. As the use of operational modal analysis in the context of structural health monitoring is a subject under active research, this theme is also introduced and briefly exemplified with data continuously recorded at the same bridge. (C) 2010 Elsevier Ltd. All rights reserved

Author's manuscript version. The final published version is available via the publisher at doi:10.1016/j.engstruct.2010.02.034. Copyright © 2010 Elsevier Ltd. All rights reserved.An ambient vibration survey of the Humber Bridge was carried out in July 2008 by a combined\ud team from the UK, Portugal and Hong Kong. The exercise had several purposes that included\ud the evaluation of current technology for instrumentation and system identification and the\ud generation of an experimental data set of modal properties to be used for validation and\ud updating of finite element models for scenario simulation and structural health monitoring. The\ud exercise was conducted as part of a project aimed at developing online diagnosis capabilities for\ud three landmark European suspension bridges.\ud Ten stand-alone triaxial acceleration recorders were deployed at locations along all three spans\ud and in all four pylons during five days of consecutive one-hour recordings. Time series\ud segments from the recorders were merged, and several operational modal analysis techniques\ud were used to analyse these data and assemble modal models representing the global behavior of\ud the bridge in all three dimensions for all components of the structure.\ud The paper describes the equipment and procedures used for the exercise, compares the\ud operational modal analysis (OMA) technology used for system identification and presents\ud modal parameters for key vibration modes of the complete structure.\ud Results obtained using three techniques: natural excitation technique/eignsystem realization\ud algorithm, stochastic subspace identification and p-LSCF, are compared among themselves and\ud with those obtained from a 1985 test of the bridge, showing few significant modal parameter\ud changes over 23 years in cases where direct comparison is possible\ud The measurement system and the much more sophisticated OMA technology used in the present\ud test show clear advantages necessary due to the compressed timescales compared to the earlier\ud exercise. Even so, the parameter estimates exhibit significant variability between different\ud methods and variations of the same method, while also varying in time and having inherent\ud variability.Engineering and Physical Sciences Research Council (EPSRC

This article will present and discuss the approach and the first results of a long-term dynamic monitoring campaign on an offshore wind turbine in the Belgian North Sea. It focuses on the vibration levels and modal parameters of the fundamental modes of the support structure. These parameters are crucial to minimize the operation and maintenance costs and to extend the lifetime of offshore wind turbine structure and mechanical systems. In order to perform a proper continuous monitoring during operation, a fast and reliable solution, applicable on an industrial scale, has been developed. It will be shown that the use of appropriate vibration measurement equipment together with state-of-the art operational modal analysis techniques can provide accurate estimates of natural frequencies, damping ratios, and mode shapes of offshore wind turbines. The identification methods have been automated and their reliability has been improved, so that the system can track small changes in the dynamic behavior of offshore wind turbines. The advanced modal analysis tools used in this application include the poly-reference least squares complex frequency-domain estimator, commercially known as PolyMAX, and the covariance-driven stochastic subspace identification method. The implemented processing strategy will be demonstrated on data continuously collected during 2 weeks, while the wind turbine was idling or parked.