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Mechanical systems and signal processing v.103, 2018년, pp.174 - 195   SCIE
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On-line Bayesian model updating for structural health monitoring

Rocchetta, Roberto (Institute of Risk and Uncertainty, University of Liverpool, L69 3GQ Liverpool, United Kingdom ); Broggi, Matteo (Institute for Risk and Reliability, Leibniz Universitt Hannover, Callinstr. 34, 30167 Hannover, Germany ); Huchet, Quentin (Université ); Patelli, Edoardo (Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal F-63000, Clermont-Ferrand, France );
  • 초록  

    Abstract Fatigue induced cracks is a dangerous failure mechanism which affects mechanical components subject to alternating load cycles. System health monitoring should be adopted to identify cracks which can jeopardise the structure. Real-time damage detection may fail in the identification of the cracks due to different sources of uncertainty which have been poorly assessed or even fully neglected. In this paper, a novel efficient and robust procedure is used for the detection of cracks locations and lengths in mechanical components. A Bayesian model updating framework is employed, which allows accounting for relevant sources of uncertainty. The idea underpinning the approach is to identify the most probable crack consistent with the experimental measurements. To tackle the computational cost of the Bayesian approach an emulator is adopted for replacing the computationally costly Finite Element model. To improve the overall robustness of the procedure, different numerical likelihoods, measurement noises and imprecision in the value of model parameters are analysed and their effects quantified. The accuracy of the stochastic updating and the efficiency of the numerical procedure are discussed. An experimental aluminium frame and on a numerical model of a typical car suspension arm are used to demonstrate the applicability of the approach. Highlights Efficient and robust procedure is proposed for on-line damage identification. Uncertainty may reduce the system health monitoring effectiveness. Stochastic Bayesian updating allows to deal with uncertainty and imprecision. Multiple likelihood functions are considered, noise and model imprecision quantified. Numerical and experimental examples show the applicability of the approach.


  • 주제어

    Bayesian model updating .   Real-time damage detection .   On-line health monitoring .   Fatigue crack .   Uncertainty .   Artificial neural networks .   Suspension arm .   Aluminium frame.  

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