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Model assisted probability of detection applied to guided wave imaging for structural health monitoring

Authors: Olivier Mesnil (CEA LIST) , Roberto Miorelli (CEA LIST) , Xavier Artusi (CEA LIST) , Pierre Calmon (CEA LIST) , Bastien Chapuis (CEA LIST) , Oscar D’Almeida (SAFRAN Tech)

  • Model assisted probability of detection applied to guided wave imaging for structural health monitoring

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    Model assisted probability of detection applied to guided wave imaging for structural health monitoring

    Authors: , , , , ,

Abstract

In Guided Wave Structural Health Monitoring (GW-SHM), reliability and performance demonstration is one of the main challenge to overcome to ensure industry adoption. However, the cost of computing a Probability of Detection (POD) from experimental data is much higher in SHM than in NDE. In addition, performance demonstration metrics must be rethought for SHM because of data dependency between the successive acquisitions. This work presents the computation of a POD metric of a GW-SHM system, using a Model-Assisted POD (MAPOD) approach. The use of simulation enables in particular a large coverage of possible configurations and the creation of independent datasets. The studied application case is the inspection of an aluminum panel instrumented by 8 piezoelectric transducers for Guided Wave Imaging (GWI). The defect is a circular through hole. The POD is computed as a function of the defect size, taking into account the following variabilities: defect position and morphology, temperature of inspection, degradation of the sensors and measurement noise. In order to quickly compute the POD for various input parameter distributions, a meta-model of the configuration is built from simulation results obtained with the CIVA software.

How to Cite:

Mesnil, O. ., Miorelli, R. ., Artusi, X. ., Calmon, P. ., Chapuis, B. . & D’Almeida, O. ., (2019) “Model assisted probability of detection applied to guided wave imaging for structural health monitoring”, Review of Progress in Quantitative Nondestructive Evaluation .

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Published on
03 Dec 2019
Peer Reviewed
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