Sensitivity driven robust vibration-based damage diagnosis under uncertainty through hierarchical Bayes time-series representations
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This work addresses the problem of vibration-based damage detection on structures operating under significant levels of uncertainty originating from variable environmental and operational conditions. For this purpose, a Gaussian Process Regression Vector AR (GPR-VAR) model is postulated for the representation of the vibration response of a structure at multiple measurement locations as a function of the uncertain inputs. The GPR-VAR model is a hierarchical Bayes representation associating the vibration response, the model parameters and the uncertain inputs. The Bayesian framework further helps to quantify the confidence attributed to the decision conditioned on the quality of the training set. The workings of the method are demonstrated via a simulated wind turbine blade driven by turbulent wind excitation, with uncertainty being introduced by changing temperatures and wind speeds. Show more
Journal / seriesProcedia Engineering
Pages / Article No.
SubjectVibration-based damage detection; uncertainty; Gaussian process regression; hierarchical Bayes
Organisational unit03890 - Chatzi, Eleni / Chatzi, Eleni
NotesThis work was made with the support of the ETH Zurich Postdoctoral Fellowship FEL-45 14-2 “A data-driven computational framework for damage identification and life-cycle management of wind turbine facilities”.
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