A Damage Detection Approach for Axially Loaded Beam-like Structures Based on Gaussian Mixture Model
| Autor: | Francescantonio Lucà, Stefano Manzoni, Francesco Cerutti, Alfredo Cigada |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
real damage
tie-rods structural health monitoring Normal Distribution beam-like structures unsupervised learning unsupervised data clustering gaussian mixture model mahalanobis squared distance Biochemistry Atomic and Molecular Physics and Optics Analytical Chemistry Cluster Analysis Electrical and Electronic Engineering Instrumentation Algorithms |
| Zdroj: | Sensors; Volume 22; Issue 21; Pages: 8336 |
| Popis: | Axially loaded beam-like structures represent a challenging case study for unsupervised learning vibration-based damage detection. Under real environmental and operational conditions, changes in axial load cause changes in the characteristics of the dynamic response that are significantly greater than those due to damage at an early stage. In previous works, the authors proposed the adoption of a multivariate damage feature composed of eigenfrequencies of multiple vibration modes. Successful results were obtained by framing the problem of damage detection as that of unsupervised outlier detection, adopting the well-known Mahalanobis squared distance (MSD) to define an effective damage index. Starting from these promising results, a novel approach based on unsupervised learning data clustering is proposed in this work, which increases the sensitivity to damage and significantly reduces the uncertainty associated with the results, allowing for earlier damage detection. The novel approach, which is based on Gaussian mixture model, is compared with the benchmark one based on the MSD, under the effects of an uncontrolled environment and, most importantly, in the presence of real damage due to corrosion. |
| Databáze: | OpenAIRE |
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