Feature selection techniques for identifying the most relevant damage indices in SHM using Guided Waves
Résumé
Feature selection techniques aim to evaluate feature’s importance and select the most relevant ones. This paper concerns the selection of features in order to perform a reliable Structural Health Monitoring by means of ultrasonic guided waves technique. The current case of study deals with the health monitoring of pipelines. A corrosion-like defect was machined in a full-scale tube and then its size was increased in five steps. Their cross-section areas (CSA) go from less than 1% to around 4.5%. To get a high accur acy, a 3D laser scanner was used to measure these CSAs. Many signal features were extracted from the ultrasonic signals. An algorithm, called sequential forward feature selection, was applied on these features to select the most discriminating ones. For the ease of the reader, a background of feature selection algorithms is presented. Damage detection procedure, basing on the Mahalanobis distance, is described. The obtained results show that all defect steps were successfully detected even the smallest one.