动态多群粒子群优化稀疏分解在薄涂层超声测厚中的应用

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振动与冲击 ›› 2025, Vol. 44 ›› Issue (1) : 61-69.

振动理论与交叉研究

动态多群粒子群优化稀疏分解在薄涂层超声测厚中的应用

刘易奕1，黄华1，王志刚2，王海涛3，卢超1，李秋锋*1

作者信息 +

Application of dynamic multi-swarm particle swarm optimization and sparse decomposition in ultrasonic thickness measurement of thin coating#br#

LIU Yiyi1, HUANG Hua1, WANG Zhigang2, WANG Haitao3, LU Chao1, LI Qiufeng*1

Author information +

文章历史 +

摘要

基于稀疏分解匹配追踪算法将装配式钢结构防护涂层超声检测信号表示在过完备Gabor时频库中，进一步提取涂层的时域信息来获得涂层的厚度信息。针对匹配追踪算法复杂度高、计算量庞大的问题，利用动态多群粒子群算法收敛快寻优能力强的特性对匹配追踪算法进行优化。基于混沌策略生成惯性权重，并将学习因子和惯性权重通过三角函数关系联立在一起，而在位置更新中增加时间因子和混沌扰动策略的影响因素，平衡了算法的局部寻优和全局寻优能力。仿真与实验表明，改进后的算法检测精度得到较大提升，能够满足实际应用，并且极大地提升了稀疏分解运算的效率，与金相检测结果对比，防火涂层检测相对误差为-4.65%，防腐涂层的检测相对误差为1.33%。

Abstract

Based on the sparse decomposition matching pursuit algorithm, the ultrasonic testing signal of the protective coating of fabricated steel structures is represented in the over complete Gabor time-frequency library, and the time domain information of the coating is further extracted to obtain the thickness information of the coating. In order to solve the problem of high complexity and huge computation of the matching pursuit algorithm, the dynamic multi swarm particle swarm optimization algorithm is used to optimize the matching pursuit algorithm. The inertia weights are generated based on the chaotic strategy, and the learning factor and inertia weights are coupled together with an over trigonometric relationship, while the time factor and the influence factor of the chaotic perturbation strategy are added in the position update, balancing the local search and global search ability of the algorithm. Simulations and experiments show that the improved algorithm has greatly improved the detection accuracy, which can satisfy the practical applications, and has greatly improved the efficiency of the sparse decomposition operation, and compared with the metallographic inspection results, the relative error of the detection of the fireproof coating is -4.65%, and that of the detection of the anticorrosive coating is 1.33%.

导出引用

刘易奕1, 黄华1, 王志刚2, 王海涛3, 卢超1, 李秋锋1. 动态多群粒子群优化稀疏分解在薄涂层超声测厚中的应用[J]. 振动与冲击, 2025, 44(1): 61-69

LIU Yiyi1, HUANG Hua1, WANG Zhigang2, WANG Haitao3, LU Chao1, LI Qiufeng1. Application of dynamic multi-swarm particle swarm optimization and sparse decomposition in ultrasonic thickness measurement of thin coating#br#[J]. Journal of Vibration and Shock, 2025, 44(1): 61-69

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