Manifold learning algorithms applied to structural damage classification
| Autor: | León Medina, Jersson Xavier, Anaya Vejar, Maribel, Tibaduiza Burgos, Diego Alexander, Pozo Montero, Francesc |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Sísmica i Dinàmica Estructural, Universitat Politècnica de Catalunya. Departament de Matemàtiques, Universitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Automatic control
Manifold learning Structural health monitoring Matemàtiques i estadística::Investigació operativa::Programació matemàtica [Àrees temàtiques de la UPC] Machine learning 70 Mechanics of particles and systems::70Q05 Control of mechanical systems [Classificació AMS] Feature extraction Damage classification Dimensionality reduction Control automàtic |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| DOI: | 10.22055/JACM.2020.33055.2139 |
| Popis: | A comparative study of four manifold learning algorithms was carried out to perform the dimensionality reduction process within a proposed methodology for damage classification in structural health monitoring (SHM). Isomap, locally linear embedding (LLE), stochastic proximity embedding (SPE), and laplacian eigenmaps were used as manifold learning algorithms. The methodology included several stages that comprised: data normalization, dimensionality reduction, classification through K- Nearest Neighbors (KNN) machine learning model and finally holdout cross-validation with 25% of data for training and the remaining 75% of data for testing. Results evaluated in an experimental setup showed that the best classification accuracy was 100% when the methodology uses isomap algorithm with a hyperparameter k of 170 and 8 dimensions as a feature vector at the input to the KNN classification machine. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|