A Bayesian Framework for Crack Detection in Structural Components Under Dynamic Excitation

Abstract

Structures under dynamic excitation can undergo phenomena of crack growth and fracture. For safety reasons, it is of key importance to be able to detect and classify these cracks before the structural failure. Additionally, the cracks will also change the dynamic behaviour of the structures, impacting their performance. Here, a Bayesian model updating procedure has been introduced for the detection of crack location and length on a numerical model. A high-fidelity finite element model has been used to simulate experimental data, by inserting cracks of different lengths in different locations and obtaining reference frequency response function. Then, a low fidelity model has been used in the Bayesian framework to infer the crack location and length by comparing the dynamic responses. It is shown that the proposed technology can be successfully adopted as a structural health monitoring tool.