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http://dx.doi.org/10.12989/sss.2010.6.3.335

Vibration-based structural health monitoring using large sensor networks  

Deraemaeker, A. (ULB, Active Structures Laboratory)
Preumont, A. (ULB, Active Structures Laboratory)
Reynders, E. (KUL, Department of Civil Engineering)
De Roeck, G. (KUL, Department of Civil Engineering)
Kullaa, J. (Aalto University School of Science and Technology)
Lamsa, V. (Aalto University School of Science and Technology)
Worden, K. (Department of Mechanical Engineering, University of Sheffield)
Manson, G. (Department of Mechanical Engineering, University of Sheffield)
Barthorpe, R. (Department of Mechanical Engineering, University of Sheffield)
Papatheou, E. (Department of Mechanical Engineering, University of Sheffield)
Kudela, P. (IFFM, Polish Academy of Science)
Malinowski, P. (IFFM, Polish Academy of Science)
Ostachowicz, W. (IFFM, Polish Academy of Science)
Wandowski, T. (IFFM, Polish Academy of Science)
Publication Information
Smart Structures and Systems / v.6, no.3, 2010 , pp. 335-347 More about this Journal
Abstract
Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.
Keywords
structural health monitoring (SHM); vibration-based methods; sensor networks; machine learning; lamb waves;
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