TY - JOUR

T1 - On-line Bayesian model updating for structural health monitoring

AU - Rocchetta, Roberto

AU - Broggi, Matteo

AU - Huchet, Quentin

AU - Patelli, Edoardo

N1 - Funding information: The authors would like to acknowledge the gracious support of this work through the EPSRC and ESRC Centre for Doctoral Training on Quantification and Management of Risk Uncertainty in Complex Systems Environments Grant No. ( EP/L015927/1 ). The authors also thank Dr. Beaurepaire Pierre for providing the extended finite element model of the car suspension arm and Liang Peng for sharing the aluminium frame simulated data and experimental measurements.

PY - 2018/3/15

Y1 - 2018/3/15

N2 - 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.

AB - 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.

KW - Aluminium frame

KW - Artificial neural networks

KW - Bayesian model updating

KW - Fatigue crack

KW - On-line health monitoring

KW - Real-time damage detection

KW - Suspension arm

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85033589295&partnerID=8YFLogxK

U2 - 10.1016/j.ymssp.2017.10.015

DO - 10.1016/j.ymssp.2017.10.015

M3 - Article

AN - SCOPUS:85033589295

VL - 103

SP - 174

EP - 195

JO - Mechanical Systems and Signal Processing

JF - Mechanical Systems and Signal Processing

SN - 0888-3270

ER -