On-line Bayesian model updating for structural health monitoring

Roberto Rocchetta, Matteo Broggi, Quentin Huchet, Edoardo Patelli (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)174-195
Number of pages22
JournalMechanical Systems and Signal Processing
Volume103
DOIs
Publication statusPublished - 15 Mar 2018
Externally publishedYes

Keywords

  • 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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