Feature map field fusion of guided wave vibrometer data for increased fault localization capability

This paper demonstrates how the fusion of information from different sensors and various processing routines can increase the reliability of damage localization using guided waves for plate-like structures. To this end, a new data processing approach is proposed: vibrometer-based guided wave measurements are used to form feature maps, which are later subjected to an image-processing-based decision fusion framework that merges data acquired from various wave emitters. This approach increases the reliability of damage localization, including the accuracy of its location and an improved signal-to-noise ratio compared to using just one wave source. In addition, the possibility of feature map resolution reduction is investigated. This latter research allows for the estimation of the breakpoints on the measurement grid size. The research is based on a laboratory experiment involving an aluminum alloy plate with multiple damages of different sizes. The paper provides a thorough verification of the method’s efficiency concerning damage size and measurement grid density.