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In the realm of structural health monitoring (SHM) for plate-like structures, the need for accurate and reliable damage detection and identification techniques is paramount. This paper explores the application of decision fusion algorithms to vibrometric data, with a focus on increasing the reliability of damage assessment for such structures. The methodology involves leveraging diverse input data sources, including variations in data processing algorithms, excitation parameters, and multiple measurements. By amalgamating these sources of diversity, we aim to mitigate measurement noise and amplify damage indications, ultimately enhancing the precision of the final diagnostic outcome. The study presents results obtained from laser vibrometry measurements on a 10 mm thick aluminum sample containing flat-bottom holes of varying diameters. A network of 12 PZT transducers was used to excite elastic waves, with impulse and chirp excitation techniques employed. Convolution techniques using OpenCV libraries were applied for data processing and fusion. The outcomes reveal a significant reduction in background noise, demonstrating the effectiveness of the fusion approach. Furthermore, the presentation outlines a roadmap for the future development of this methodology, which includes automating data processing, evaluating the impact of damage indicators, processing parameters and usage of different excitation transducers.
Dworakowski et al. (Sat,) studied this question.