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Sequential Importance Sampling Based on a Committee of Artificial Neural Networks for Posterior Health Condition Estimation

Abstract : The output of real-time diagnostic systems based on the interpretation of signals from a sensor network is often affected by very large uncertainties if compared with local non-destructive testing methods. Sequential Importance Resampling (SIR) is used in this study to filter the output distribution from a committee of Artificial Neural Networks. The methodology is applied to a helicopter panel subject to fatigue crack propagation. Strain signals are acquired during crack evolution and a diagnostic unit trained on simulated experience provides damage assessment in real-time. This information is filtered through a SIR routine, providing model identification, model parameter estimation and crack length probability density function updating, conditioned on the observations at discrete time steps.
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https://hal.inria.fr/hal-01021052
Contributor : Anne Jaigu <>
Submitted on : Wednesday, July 9, 2014 - 8:43:16 AM
Last modification on : Tuesday, August 13, 2019 - 11:10:03 AM
Long-term archiving on: : Thursday, October 9, 2014 - 10:51:32 AM

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Claudio Sbarufatti. Sequential Importance Sampling Based on a Committee of Artificial Neural Networks for Posterior Health Condition Estimation. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01021052⟩

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