The Astir Glider Wing Dataset for Population-Based SHM

Applications of structural health monitoring (SHM) often use vibration data to detect when damage occurs in structures, by detecting changes in the modal properties. Damage causes changes in material/structural properties which affect the modal characteristics; however, inconsequential changes in environment can reduce the capability of the damage detector, as they also affect the modal characteristics. Furthermore, small differences between nominally identical structures may also manifest as changes in the modal characteristics, thus making fully enveloping baselines difficult to obtain for an entire population of structures. This challenge provides the motivation for the field of population-based SHM (PBSHM), where the population is considered as a whole, in order to generate a more robust damage detection strategy. An experimental campaign was conducted to collect vibration data on four nominally identical Grob G102 Astir glider wings, three of which were “healthy” and one of which was damaged; this was done to generate a dataset which can be used to develop and test PBSHM methods. The experimental campaign included testing over a range of temperatures, and included a variety “pseudo-damage” states, where masses were added to the structures. This paper highlights the challenges faced, which motivate PBSHM by showing some examples of the data collected, including frequency response data and estimated modal characteristics. In addition, the paper shows some preliminary work which aims to separate out data of individual structures or states, from data of the full population.