A Diagnostic Imaging Technique for Identification of Structural Damage Using Hybrid Features of Lamb Wave Signals

Diagnostic imaging using Lamb waves, such as Lamb wave tomography, has been attracting increasing preference because it yields an easily interpretable map concerning the 'health' state of the structure. It is however envisaged that to construct a tomogram is often at the expenses of using a large number of sensors to scan the entire area carefully, therefore limiting its application for online structural health monitoring (SHM). On the other hand, when dealing with orientation-specific or sharp-angled damage ( e.g., a crack or a notch), a challenging issue is that such sort of damage often exerts strong directivity to wave propagation, posing difficulty in damage identification. In this thesis work, a novel probability-based diagnostic imaging (PDI) approach was developed with an aim to circumvent the above-addressed deficiencies. First, three fundamental issues relevant to Lamb-wave-based-PDI were interrogated: a) effect of the orientation of damage with sizable length in a particular dimension (orientation-specific damage) on Lamb wave propagation, b) attenuation of Lamb wave as propagation and its compensation, and c) influential area of damage on wave sensing path. Both finite element (FE) simulation and experimental validation were conducted and results were compared. Results arising from these fundamental studies served as the knowledge basis to develop PDI approach. Second, based on the correlations established in the above fundamental studies between (1) damage location and ToFs extracted from signals, (2) intensity of signal energy scattered by damage and damage orientation, and (3) signal correlation and damage severity, a novel PDI approach was developed, in conjunction with use of an active sensor network. To supplement the possible insufficiency of signal features for high-precision identification, a novel concept of ' virtual sensing' was established, facilitating extraction of rich signal features. The approach was validated by predicting representative damage scenarios including a through-thickness crack (strong orientation-specific damage), an L-shape crack (strong orientation-specific damage), polygonal damage (multi-edge damage) and multi-damage in aluminium plates. Accurate identification results for typical damage cases have demonstrated the effectiveness of the developed PDI approach in quantitatively visualising structural damage, regardless of its shape and number, by highlighting its individual edges in an easily interpretable binary image.