Gaussian mixture model for time series-based structural damage detection

• Autorzy: Słoński M.
• Języki publikacji: EN

Abstrakty

EN

In this paper, a time series-based damage detection algorit hm is proposed using Gaussian mixture model (GMM) and expectation maximization (EM) framework. The vib ration 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 detec tion 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

Słowa kluczowe

EN

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

PL

dynamika; zagadnienie odwrotne; obserwacja ciągła; wykrywanie uszkodzeń; model Gaussa; novelty detection

• Wydawca: Instytut Podstawowych Problemów Techniki PAN
• Czasopismo: Computer Assisted Methods in Engineering and Science
• Rocznik: 2012
• Tom: Vol. 19, no. 4
• Strony: 331--338
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr.

Twórcy

autor

Słoński M.

• Cracow University of Technology, Institute for Computational Civil Engineering, Warszawska 24, 31-155 Kraków, Poland,

Bibliografia

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