基于遗传算法优化稀疏分解的防护涂层测厚研究

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (11) : 279-287.

论文

刘易奕1，陈尧1，李秋锋1，王志刚2，王海涛3

作者信息 +

Protective coating thickness measurement based on genetic algorithm optimization in sparse decomposition

LIU Yiyi1, CHEN Yao1, LI Qiufeng1, WANG Zhigang2, WANG Haitao3

Author information +

文章历史 +

摘要

针对采用高频超声水浸法检测装配式钢结构的防护涂层厚度的仿真实验中，防护涂层的界面反射回波相互混叠，导致无法提取涂层的时域信息的问题，利用基于遗传算法优化稀疏分解中的匹配追踪过程对混叠信号进行分离与重构。该算法在构建的Gabor原子库中，利用遗传算法对最佳原子参数的搜索过程进行优化，同时将传统稀疏分解匹配追踪算法中的内积运算优化为互相关运算，从而优化了稀疏分解的运算效率。与金相检测涂层厚度的结果相比较，该改进算法的检测相对误差为2.50%，在可接受的范围内，且较传统稀疏分解匹配追踪算法5.01%的检测相对误差的检测精度高，同时运算速度得到较大提升。

Abstract

In the simulation experiment of using high-frequency ultrasonic water immersion method to detect the thickness of protective coatings of assembled steel structures, the interfacial reflected echoes of the protective coatings are overlapped with each other, resulting in the inability to extract the time-domain information of the coatings, and the genetic algorithm-based optimization of the matching tracking process in the sparse decomposition is used to separate and reconstruct the overlapped signals. The algorithm optimizes the search process of the optimal atomic parameters in the constructed Gabor atom library by using genetic algorithm, and optimizes the inner product operation in the traditional sparse decomposition matching tracking algorithm to a mutual correlation operation, thus optimizing the operational efficiency of the sparse decomposition. Compared with the results of metallographic inspection of coating thickness, the inspection relative error of this improved algorithm is 2.50%, which is within the acceptable range and has a higher detection accuracy than the traditional sparse decomposition matching tracking algorithm with a large inspection relative error of 5.01%, while the computing speed is greatly improved.

导出引用

刘易奕1，陈尧1，李秋锋1，王志刚2，王海涛3. 基于遗传算法优化稀疏分解的防护涂层测厚研究[J]. 振动与冲击, 2024, 43(11): 279-287

LIU Yiyi1, CHEN Yao1, LI Qiufeng1, WANG Zhigang2, WANG Haitao3. Protective coating thickness measurement based on genetic algorithm optimization in sparse decomposition[J]. Journal of Vibration and Shock, 2024, 43(11): 279-287

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