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.

Aeroelastic scaling of flying demonstrators: mode tracking technique

“…This objective function is equivalent to the one proposed by Giraldo [21], used in the field of health monitoring of civil structures. It is also similar to the one proposed by Morlier et al [22]. For the scaled frequency matching, we set an equality constraint for each scaled frequency, as used by Ricciardi et al [9] and defined in equation ( 3).…”

Multi-objective sensor placement optimization and damage identification for an aircraft wing using Lichtenberg algorithm

“…Haniš and Hromčík (2011) published their study on the positioning optimization of sensors for BWB (Blended Wing Body) aircraft using algorithms based on the Effective Independence (EfI) metric to maximize the information obtained by the sensors while minimizing their installed quantity. Morlier et al. (2018) also published their research for the optimized positioning of sensors on aircraft wings, but this time using the adaptive Kriging metamodeling approach called Efficient Global Optimization – Sensor Placement Optimization (EGO-SPO), in addition to the Modal Assurance Criterion (MAC) metric.…”