Gaussian mixture model for time series-based structural damage detection

  • Marek Słoński


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.


dynamics, inverse problems, structural monitoring, damage detection, mixture model, novelty detection,


Jan 25, 2017
How to Cite
SŁOŃSKI, Marek. Gaussian mixture model for time series-based structural damage detection. Computer Assisted Methods in Engineering and Science, [S.l.], v. 19, n. 4, p. 331-338, jan. 2017. ISSN 2299-3649. Available at: <>. Date accessed: 23 jan. 2022.