Generating Reduced-Order Image Data and Detecting Defect Map on Structural Components Using Ultrasonic Guided Wave Scan

Abstract

<div class="section abstract"><div class="htmlview paragraph">The paper presents a theoretical framework for the detection and first-level preliminary identification of potential defects on aero-structure components by employing ultrasonic-guided wave-based structural health monitoring strategies, systems and tools. In particular, we focus our study on ground inspection using a laser-Doppler scan of the surface velocity field, which can also be partly reconstructed or monitored using point sensors and actuators structurally integrated. Using direct wavefield data, we first question the detectability of potential defects of unknown location, size, and detailed features. Defects could be manufacturing defects or variations, which may be acceptable from a design and qualification standpoint; however, those may cause significant background signal artefacts in differentiating structure progressive damage or sudden failure like impact-induced damage and fracture. We consider the surface velocity field over continuous time stamps obtained from laser-doppler scan experiments using surface-integrated piezoelectric transducers on a composite panel. We consider such likely uncertainty in material properties and measurement noise issues while studying specific defects such as delamination. We use the physics of wave interaction with these different defects to show the detectability of hotspots and their preliminary identification ability, whether they could be material manufacturing uncertainty or structure damage such as delamination. We then discuss advanced algorithms based on reduced-order imaging techniques with certain invariance properties, such as signal phase change associated with stationary features compared to moving features or noise. A novel first-order cross-correlation using surface velocity similarity is developed and applied to study the stationary and non-stationary features in the image. The defect map is then generated from the high-dimensional temporal field data, where a detection threshold is used to define the defect hot spot. To this end, the nature of wave mode conversion and attenuation across these defects are discussed, which are important precursors for full-fledge SHM system-based automated detection.</div></div>