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

Abstract : 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.
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Submitted on : Thursday, July 10, 2014 - 9:59:30 AM
Last modification on : Wednesday, August 7, 2019 - 12:18:06 PM
Long-term archiving on: : Friday, October 10, 2014 - 11:01:38 AM


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  • HAL Id : hal-01022053, version 1



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⟩



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