Time frequency analysis of elastic wave PSO OMP for defects in flat steel of down conductors

• Published in: Scientific reports Vol. 15; no. 1; pp. 17518 - 15
• Main Authors: Zhang, Jing, Liu, Wei, Bao, Minghui, Yang, Peng, Liu, Xuhua, Fu, Zhihong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/4077; Algorithms; Data acquisition; Data processing; Defect localization of flat steel conductor; Denoising; Elastic waves; Environmental factors; Excavation; Frequency analysis; Frequency dependence; Humanities and Social Sciences; Localization; Maintenance costs; multidisciplinary; Noise reduction; PSO-OMP optimization algorithm; Science; Science (multidisciplinary); Signal processing; SPWVD; PSO-OMP optimization algorithm; Defect localization of flat steel conductor; Denoising; SPWVD
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-02863-6

This article proposes a method based on elastic wave data reconstruction and denoising to solve the problem of defect localization in grounding grid down conductor flat steel. Due to environmental factors, cross media propagation, and interference from excitation sources, noise is coupled into the original signal during data acquisition, seriously affecting signal processing. To solve this problem, this paper uses the Particle Swarm Optimization Orthogonal Matching Pursuit (PSO-OMP) algorithm to reconstruct the signal, significantly reducing noise. A detailed analysis of the computational cost was conducted using different parameters of PSO-OMP, and a comparison was made from the statistical data of reconstructed signals. Finally, the optimal parameters for the PSO-OMP algorithm were determined. At the same time, this article compared several different traditional denoising algorithms, AI denoising algorithm, and PSO-OMP reconstructed signals. Then calculate the correlation function of the reconstructed signal echo and apply a smooth pseudo Wigner Ville distribution for time-frequency analysis. This method can identify the time delay and corresponding frequency of defect signals. Finally, by combining time delay and distance between sensors, the defect location can be accurately calculated. Actual testing has shown that compared to data without denoising, the relative error of the defect location measured by this method is less than 10%. This method provides a cost-effective solution for defect detection in grounding systems, particularly for early-stage cracks (10–25 mm) in power infrastructure. Compared to traditional excavation methods, it reduces maintenance costs by over 90% and minimizes downtime.