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Intelligent location algorithm of U-shaped boom compact array based on evolutionary strategy and clustering algorithm
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DOI：10.1016/j.ymssp.2025.112650.png)
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Abstract
En 中文
As a key load-bearing component of the crane, the health status of the boom directly affects the performance and safety of the crane. Therefore, it is especially important to develop a structural health monitoring (SHM) method for the boom. In this study, a compact array intelligent location algorithm based on combined time-of-flight method (CTFM) is proposed and applied to the defect detection of U-shaped boom. In this algorithm, direct time-of-flight (DTFM) and time-of-flight difference (TFDM) are combined to build a scattering point evaluation model to screen scattering point, and evolutionary strategies and clustering algorithms are used to search and locate scattering points quickly. The algorithm transforms the classical imaging problem into the estimation and search scattering points, and uses individual distribution to identify the defect position. First of all, the numerical simulation proves that the proposed intelligent location algorithm has reliable performance in detecting defects of different shapes and sizes. Compared with the traditional ellipse imaging algorithm, the compact array intelligent location algorithm based on CTFM shows advantages in positioning resolution, positioning accuracy and algorithm execution efficiency. Subsequently, through the analysis of the experimental signals of doublehole defect, it is further verified that the algorithm can not only improve the defect positioning accuracy and algorithm execution efficiency, but also effectively remove the interference of direct waves by adjusting parameters. Finally, the influence of parameter setting on the detection results is studied, and the optimal parameter setting of the algorithm is discussed. This algorithm provides a tool for multi-defect detection of U-shaped boom.
Keywords:
U-shaped boom
Ultrasonic guided waves
Structural health monitoring
Intelligent location algorithm
Imaging algorithm
Journal
IF:
8.9
Papers:
1.3W
Citations:
6.6W