基于灰狼算法的轨道交通钢轨缺陷的稀疏全聚焦成像方法
钱鲁斌吴鸿博高春翔韦正波邢宇辉
Sparse Total Focus Imaging of Rail Transit Rail Defects Based on Grey Wolf Algorithm
QIAN LubinWU HongboGAO ChunxiangWEI ZhengboXING Yuhui
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作者信息:上海工程技术大学城市轨道交通学院， 201620， 上海
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Affiliation:School of Urban Rail Transportation, Shanghai University of Engineering Science, 201620, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.03.021
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中图分类号/CLCN:U213.4+3
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栏目/Col:研究报告
摘要:
［目的］采用超声相控阵技术可实现轨道交通钢轨缺陷的高精度成像检测，但广泛应用的全矩阵捕获全聚焦成像方法存在计算耗时长、实时性不高的缺点。为缩短成像计算时间，采用稀疏矩阵替代全矩阵进行超声成像，但传统智能优化算法在解决稀疏阵列设计问题时存在收敛慢、易陷入局部最优的问题。为提高收敛性能和全局搜索能力，提出基于灰狼算法的轨道交通钢轨缺陷的稀疏全聚焦成像方法。［方法］对优化算法下的稀疏阵列性能进行了分析；利用超声相控阵仪器在钢轨试样上采集超声信号，通过稀疏矩阵进行全聚焦成像，以分析成像质量和成像时间。［结果及结论］利用优化算法得到的稀疏阵列具有较高的旁瓣抑制力，PSL(峰值旁瓣水平)可达到-12.83 dB；当PSL阈值为-6 dB时，稀疏阵列主瓣宽度与全阵列2.8°的主瓣宽度相当；稀疏率为75%时，钢轨成像性能指标质量接近全阵列，成像时间缩短了56.35%。
Abstracts:
［Objective］ Using the ultrasonic phased array technology can realize high-precision imaging detection of rail transit rail defects. However, the widely used FMC-TFM (full matrix capture-total focusing method) has the disadvantages of long computation time and low real-time performance. To reduce imaging calculation time, the sparse matrix is used to replace the full matrix for ultrasonic imaging. But the traditional intelligent optimization algorithm faces the trouble of slow convergence and is easy to fall into the local optimum when solving the sparse array design problem. Therefore, the sparse total focusing imaging method of rail defects based on the grey wolf algorithm is proposed to improve convergence performance and global search ability. ［Method］ The sparse array performance under optimization algorithm is analyzed. The ultrasonic phased array instrument is used to collect ultrasonic signals on the rail samples. Total focusing imaging is realized through the sparse matrix to analyze the imaging quality and time. ［Result & Conclusion］ The sparse array obtained by optimization algorithm has high sidelobe inhibition, and the PSL (peak sidelobe level) can reach -12.83 dB. At the PSL threshold of -6 dB, the width of the main lobe of the sparse array is comparable to that of the main lobe of 2.8° of the full array. When the sparsity rate is 75%, the quality of the rail imaging performance indicators is close to full array, and the imaging time is shortened by 56.35%.
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