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Stochastic method for in-situ damage analysis

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  • P. Rinn
  • H. Heißelmann
  • M. Wächter
  • J. Peinke

Abstract

Based on the physics of stochastic processes we present a new approach for structural health monitoring. We show that the new method allows for an in-situ analysis of the elastic features of a mechanical structure even for realistic excitations with correlated noise as it appears in real-world situations. In particular an experimental set-up of undamaged and damaged beam structures was exposed to a noisy excitation under turbulent wind conditions. The method of reconstructing stochastic equations from measured data has been extended to realistic noisy excitations like those given here. In our analysis the deterministic part is separated from the stochastic dynamics of the system and we show that the slope of the deterministic part, which is linked to mechanical features of the material, changes sensitively with increasing damage. The results are more significant than corresponding changes in eigenfrequencies, as commonly used for structural health monitoring. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • P. Rinn & H. Heißelmann & M. Wächter & J. Peinke, 2013. "Stochastic method for in-situ damage analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(1), pages 1-5, January.
    • Handle: RePEc:spr:eurphb:v:86:y:2013:i:1:p:1-5:10.1140/epjb/e2012-30472-8
    DOI: 10.1140/epjb/e2012-30472-8
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    Cited by:

    1. Pedro G. Lind & Luis Vera-Tudela & Matthias Wächter & Martin Kühn & Joachim Peinke, 2017. "Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach," Energies, MDPI, vol. 10(12), pages 1-14, November.
    2. Wenxin Yu & Shoudao Huang & Weihong Xiao, 2018. "Fault Diagnosis Based on an Approach Combining a Spectrogram and a Convolutional Neural Network with Application to a Wind Turbine System," Energies, MDPI, vol. 11(10), pages 1-11, September.
    3. Rajabzadeh, Yalda & Rezaie, Amir Hossein & Amindavar, Hamidreza, 2016. "A robust nonparametric framework for reconstruction of stochastic differential equation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 294-304.

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    Keywords

    Statistical and Nonlinear Physics;

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