Imaging-based quantitative characterization of fatigue crack for structural integrity monitoring using nonlinear acousto-ultrasonics and active sensor networks

Abstract

The majority of today’s damage detection techniques rely substantially on linear macroscopic changes in either global vibration signatures or local wave scattering phenomena. However, damage in real-world structures often initiates from fatigue cracks at microscopic levels, presenting highly nonlinear characteristics which may not be well evidenced in these linear macroscopic changes. It is of great significance but also a great challenge to quantitatively characterize micro-fatigue cracks without terminating the normal operation of an engineering structure. This is a critical step towards automatic and online structural integrity monitoring (SIM). By exploring the nonlinearities of higher-order acousto-ultrasonic (AU) waves upon interaction with fatigue cracks, a damage characterization approach, in conjunction with use of an active piezoelectric sensor network, was established, with the particular capacity of evaluating multiple fatigue cracks at a quantitative level (including the co-presence of multiple cracks, and their individual locations and severities). Identification results were presented in pixelated images using an imaging algorithm, enabling visualization of fatigue cracks and depiction of overall structural integrity in a quantitative, rapid and automatic manner. The effectiveness of the proposed technique was demonstrated by experimentally characterizing multiple fatigue cracks near rivet holes in aluminium plates.