Hierarchical Bayes for population-based modelling of FRFs with temperature variation

Structural health monitoring (SHM), relies on evaluating how certain features change over time (such as natural frequencies), to detect changes indicative of damage. However, damage-sensitive features often respond to benign variations, such as changes in temperature, in addition to damage. These normal variations can mimic or mask damage, and can therefore affect the efficacy and generalisation of SHM technologies. Population-based SHM (PBSHM) aims to share valuable information, including normal condition and damage states, among similar structures. The aim of the current work is to investigate the effects of temperature variation and identify methods to account for these changes, using PBSHM principles. A hierarchical Bayesian approach was used to develop a combined probabilistic frequency response function (FRF) model using FRFs from a full-scale composite helicopter blade. The FRFs were computed using vibration data collected with the blade exposed to various temperatures in an environmental chamber. Functions representing how the natural frequency and modal damping changed according to temperature were determined, and the results were extrapolated to temperatures not used in model training.