Abstract
The problem of random vibration based robust damage detection for a composite aerostructure under assembly-induced uncertainty is considered. The focus is on the exploration of the performance limits and critical assessment of two unsupervised Statistical Time Series (STS) type robust methods: a Multiple Model (MM) based method and a Principal Component Analysis (PCA) based counterpart. Three progressive damage scenarios are considered, the effects of which on the structural dynamics are ‘minor’ and almost completely ‘masked’ by assembly-induced uncertainty. The assessment, based on hundreds of experiments, suggests that damage as small as 10% reduction in the tightening torque of a single bolt is detectable in a reliable way. Both methods achieve remarkably good detection performance characterized by 100% correct detection for false alarm rates of 3.5% or higher, with the MM type method exhibiting a slight edge in performance over its PCA based counterpart! Overall the unsupervised STS type robust methods are shown to effectively detect ‘minor’ damage in the presence of significant assembly-induced uncertainty!
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Notes
- 1.
Such procedures are also referred to as (vibration) ‘data normalization’.
- 2.
Bold-face upper/lower case symbols designate matrix/column–vector quantities, respectively.
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Andriosopoulou, G., Mastakouris, A., Vamvoudakis-Stefanou, K., Fassois, S. (2020). Random Vibration Based Robust Damage Detection for a Composite Aerostructure Under Assembly-Induced Uncertainty. In: Wahab, M. (eds) Proceedings of the 13th International Conference on Damage Assessment of Structures. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8331-1_61
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