Advanced pattern recognition techniques for wave-based structural health monitoring of metallic panels

Date

2018-08-24

Authors

Ebrahimkhanlou, Arvin

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Increasing loads on aging and deteriorating aerospace and naval structures, such as airplanes and marine vessels, their usage beyond the designed life, and the desire to reduce the downtime associated with their regular maintenance operations have all motivated research on structural health monitoring (SHM) methods. Among SHM methods, those based guided ultrasonic waves, which are excited and received by low-profile piezoelectric transducers, are one of the most promising candidates for detecting, localizing, and characterizing structural defects. Despite the significant development of these SHM systems, very few, if any, have been implemented in real structures. One major reason for this limited implementation is due the difficulty of the processing and interpreting the reverberation patterns of guided ultrasonic waves. Such reverberations are due to multiple reflections of the waves from structural and geometrical features, such as boundaries, stiffeners, and fasteners. Therefore, the primary goal of this research is to overcome this challenge by advancing pattern recognition techniques and analyzing the patterns of edge-reflected guided-ultrasonic reverberations in thin metallic panels. The objective is to leverage such patterns to improve the accuracy of current damage localization algorithms and reduce the number of required sensors. Specifically, two damage localization modes are considered: active ultrasonic imaging and passive acoustic emission. However, this dissertation gives more weight to the latter. For both active and passive modes, an analytical model are developed to simulate the patterns of edge-reflected, guide-ultrasonic reverberations. For the passive mode, a probabilistic framework is also developed to quantify the systematic uncertainties associated with this reflection-based localization approach. In addition, deep learning based, data-driven approaches are used to extend the application of the passive mode to metallic panels with rivet-connected stiffeners and allow characterizing the defects. For validation, experiments are conducted on rectangular aluminum panels with square-cut edges. The results show the effectiveness of the developed pattern recognition approaches in detecting, localizing, and characterizing structural defects, such as simulated fatigue cracks, with significantly fewer number of sensors. The knowledge gained in this investigation contributes to the condition awareness of metallic panels

Description

LCSH Subject Headings

Citation