Abstract
Structural dynamics is fundamentally concerned with the design, operation, and understanding of physical structures. A significant concern in the management of these, often very high-value, assets, is their state of health. When a structure sustains damage, this can have an extremely negative effect on its availability, and this will have serious implications for profitability and also the safety of any human operators or occupants. It is therefore important to implement some means of monitoring structural health so that incipient damage can be detected and remedial actions can be taken before negative consequences occur. The pertinent damage identification methodology for engineering structures is Structural Health Monitoring (SHM). This chapter presents an overview of SHM, with particular reference to implementations based on monitoring structural vibrations and waves. The main philosophy under discussion here is data-based SHM, where diagnostics are based on the interpretation of measured data directly, without recourse to physics-based models. The main technologies for carrying out data-based SHM are statistical pattern recognition and machine learning, and the chapter gives some background on these methods and provides some case studies to illustrate their use. One of the main approaches to damage detection is novelty detection, where one develops a statistical model of measured features from the undamaged structure of interest, and monitors subsequent data to see if there are deviations from the model, indicative of damage. A serious problem with this approach is that it is prone to false alarms if there are benign changes to the data, like operational or environmental variations. Such benign changes – referred to here as confounding influences, need to be compensated for, if the SHM system is to be reliable and error-free (as far as possible). The chapter considers how confounding influences arise, and how they can be removed in the data-driven context by data normalization. Finally, the chapter concludes with some discussion of how physics-based models can still have a potentially useful role in data-driven SHM.
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Fuentes, R. et al. (2022). Structural Health Monitoring and Damage Identification. In: Allemang, R., Avitabile, P. (eds) Handbook of Experimental Structural Dynamics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4547-0_23
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