Gaussian mixture model for time series-based structural damage detection
Journal Title: Computer Assisted Methods in Engineering and Science - Year 2012, Vol 19, Issue 4
Abstract
In this paper, a time series-based damage detection algorithm is proposed using Gaussian mixture model (GMM) and expectation maximization (EM) framework. The vibration time series from the structure are modelled as the autoregressive (AR) processes. The first AR coefficients are used as a feature vector for novelty detection. To test the efficacy of the damage detection algorithm, it has been tested on the pseudo-experimental data obtained from the FEM model of the ASCE benchmark frame structure. Results suggest that the presented approach is able to detect mainly major and moderate damage patterns.
Authors and Affiliations
Marek Słoński
Keywords
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