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An EGO-like Optimization Framework for Sensor Placement Optimization in Modal Analysis

Abstract : In aircraft design, Ground/Flight Vibration Tests (GVT/FVT) are conducted to extract aircraft's modal parameters (natural frequencies, damping ratios and mode shapes) also known as the modal basis. The main problem in aircraft modal identification is the large number of sensors needed, which increases operational time and costs. The goal of this paper is to minimize the number of sensors by optimizing their locations in order to reconstruct a truncated modal basis of N mode shapes with a high level of accuracy in the reconstruction. There are several methods to solve Sensors Placement Optimization (SPO) problems, but for this case an original approach has been established based on an iterative process for mode shapes reconstruction through an adaptive Kriging Metamodeling approach so-called EGO-SPO. The main idea in this publication is to solve an optimization problem where the sensors locations are variables and the objective function is defined by maximizing the trace of criteria so-called AutoMAC. The results on a 2D wing demonstrate a reduction of sensors by 30% using our EGO-SPO strategy.
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Submitted on : Monday, June 4, 2018 - 9:42:48 AM
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Joseph Morlier, Aniello Basile, Ankit Chiplunkar, M. Charlotte. An EGO-like Optimization Framework for Sensor Placement Optimization in Modal Analysis. Smart Materials and Structures, IOP Publishing, 2018, vol. 27 (n° 7), pp. 075004-075022. ⟨10.1088/1361-665X/aac12b⟩. ⟨hal-01806709⟩



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