Algorithms for Flaw Detection by SHM with Static Sensors Approach

• Published in: Macromolecular symposia. Vol. 405; no. 1
• Main Author: Iannone, Michele
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley Subscription Services, Inc 01.10.2022
• Subjects: Aircraft; Aircraft structures; Algorithms; Damage detection; Fiber optics; Finite element method; Flaw detection; Flight safety; Impact damage; Information processing; Maintenance costs; neural network; Neural networks; reverse FEM; Sensors; SHM; Strain gauges; Structural health monitoring; Wave reflection
• ISSN: 1022-1360; 1521-3900
• DOI: 10.1002/masy.202100301

Structural Health Monitoring (SHM) is a technology aimed to monitor the soundness of the structures. Applications for aircraft structure are largely investigated. The goal is to utilize the information acquired during the monitoring to save maintenance costs, improve flight safety, and design lighter structures. There are different issues investigated by SHM research, like load monitoring and impact detection. The most primarily investigated issue is damage detection, that is, developing a system that utilizes sensors to detect the damages in the structure. The damage detection can be performed with two different types of sensors: dynamic and static sensors. Dynamic sensors work by the transmission of waves through the structures. Defects are identified by deviation and reflection of the waves by damages. The SHM approach described in this work uses static sensors, like strain gages or fiber optics. The strain fields under load in pristine and damaged conditions are compared; damages are identified by evaluating the change in the strain field. Developed and patented in Leonardo Aircraft, two different algorithms for damage detection are described in this work. The algorithms are based on reverse finite element model (F.E.M.) and neural networks.