Structural Damage Classification Comparison Using Support Vector Machine and Bayesian Model Selection - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Structural Damage Classification Comparison Using Support Vector Machine and Bayesian Model Selection

Résumé

Since all damage identification strategies inevitably involve uncertainties from various sources, a higher level of characterization is necessary to facilitate decision-making in a statistically confident sense. Machine learning plays an important role in the decision-making process of damage detection, classification, and prognosis, which employs training data (or a validated model) and extracts useful information from the high-dimensional observations. This paper classifies the type of damage via support vector machine (SVM) in a supervised learning fashion, and selects the most plausible model for data interpretation. Therefore the separation of damage type and failure trajectory is transformed into a group classification process, under the influence of uncertainty. Given data observation, SVM is obtained under a training process, which characterizes the best classification boundaries for any future feature set. A rotary machine test-bed is employed, and vibration-based damage features are evaluated to demonstrate the proposed classification process.
Fichier principal
Vignette du fichier
0292.pdf (1.56 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01022053 , version 1 (10-07-2014)

Identifiants

  • HAL Id : hal-01022053 , version 1

Citer

Zhu Mao, Michael Todd. Structural Damage Classification Comparison Using Support Vector Machine and Bayesian Model Selection. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01022053⟩
127 Consultations
200 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More