Multiform damage detection via ultrasonic guided wave-machine vision heterogeneous data fusion based on fuzzy D-S evidence theory

Vollständiger Abstract

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Aiming at the difficulty of full-dimensional identification of multiform damages in aerospace and rail transit structures via single detection technology, this study proposes a heterogeneous data fusion detection method integrating ultrasonic guided wave and machine vision based on fuzzy D-S evidence theory. First, ultrasonic guided-wave signal centroid analysis and elliptical discretization imaging are used to preliminarily locate suspected damage areas. Guided by the positioning results, collaborative robots and machine vision technology (including Gaussian filtering, adaptive threshold segmentation, and morphological optimization) are employed for precise identification and quantification of surface damages. To address data randomness, fuzziness, and evidence conflicts, Gaussian membership functions are introduced to optimize evidence construction, and the Dempster rule is adopted for dual-source evidence fusion. A hidden damage location and quantification criterion based on spatial correlation hypothesis and nonlinear mapping function is also proposed. Experimental validation on 6061 aluminum alloy plate specimens shows that the method achieves 100% surface damage recognition rate and 98.3% hidden damage detection rate. The surface damage positioning error is ≤10.7 mm with size error ≤3.36%, and the hidden damage positioning error is ≤18.8 mm. The evidence conflict coefficient is below 0.3, avoiding the “belief paradox” in traditional D-S evidence theory. This method outperforms single detection technologies, providing an efficient solution for full-dimensional health monitoring of complex structures.