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Damage Detection in Tensegrity using Interacting Particle-Ensemble Kalman Filter

Neha Aswal 1 Subhamoy Sen 1 Laurent Mevel 2 
2 I4S - Statistical Inference for Structural Health Monitoring
Inria Rennes – Bretagne Atlantique , COSYS - Département Composants et Systèmes
Abstract : Tensegrity structures form a special class of truss with dedicated cables and bars, that take tension and compression, respectively. To ensure equilibrium, the tensegrity members are required to be prestressed. Over prolonged usage, the cables may lose their prestress while bars may buckle, affecting the structural stiffness as well as its dynamic properties. The stiffness of tensegrities also vary with the load even in the absence of damage. This can potentially mask the effect of damage leading to a false impression of tensegrity health. This poses a major challenge in tensegrity health monitoring especially when the load is stochastic and unknown. Present study develops a vibration based output-only time-domain approach for monitoring the health of any tensegrity in the presence of uncertainties due to ambient force and measurement noise. An Interacting Particle Ensemble Kalman Filter (IPEnKF) has been used that can efficiently monitor tensegrity health from contaminated response data. IPEnKF combines a bank of Ensemble Kalman Filters to estimate response states while running within a Particle Filter envelop that estimates a set of location based health parameters. Further to make damage detection cheaper, strain responses are used as measurements. The efficiency of the proposed methodology has been demonstrated using numerical experiments performed on a simplex tensegrity.
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https://hal.inria.fr/hal-03277243
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Submitted on : Friday, July 2, 2021 - 6:16:54 PM
Last modification on : Friday, June 17, 2022 - 1:27:50 PM
Long-term archiving on: : Sunday, October 3, 2021 - 8:33:16 PM

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Neha Aswal, Subhamoy Sen, Laurent Mevel. Damage Detection in Tensegrity using Interacting Particle-Ensemble Kalman Filter. EWSHM 2020 - 10th Workshop on Structural Health Monitoring, Jul 2020, Palermo, Italy. pp.1-10. ⟨hal-03277243⟩

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