摘要
针对超声导波检测技术用于钢轨断轨的长距离监测时,检测灵敏度低的问题,提出一种基于粒子群算法的变分模态分解的超声导波信号处理方法。以导波信号的最小包络熵为衡量指标,优化变分模态分解的参数选择,对采集到的导波信号进行滤波处理。为衡量基于粒子群算法的变分模态分解方法的去噪效果,引入小波变化方法与变分模态分解方法,将处理得到的缺陷信号进行频域分析,再将缺陷信号与背景噪声的峰峰值进行比较。实验结果表明:上述方法对断轨信号有明显的滤波作用,其效果优于小波变换和变分模态分解方法。
To deal with the low detection sensitivity of ultrasonic guided wave detection technology adopted in the long-distance monitoring of rail broken rail,a variational mode decomposition method based on particle swarm optimization is proposed.The minimum envelope entropy of guided wave signal taken as the index,the parameter selection of variational mode decomposition is optimized,and the collected guided wave signal is filtered.To measure the denoising effect of the variational mode decomposition method based on particle swarm algorithm,wavelet change method and variational mode decomposition method are introduced,the processed defect signal is analyzed in frequency domain,and the peak value of the defect signal is compared with the background noise.The experimental results show that the proposed method plays an obvious filtering role in broken rail signal,and its effect is better than that of wavelet transform and variational mode decomposition method.
作者
许溢航
王平
杨元
景丽暄
XU Yihang;WANG Ping;YANG Yuan;JING Lixuan(School of Automation Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《机械制造与自动化》
2025年第4期165-169,共5页
Machine Building & Automation
基金
江苏省重点研发计划项目(BE2021621)
江苏省工业和信息产业转型升级专项资金项目(苏工信综合[2022]115号)。
关键词
变分模态分解
粒子群算法
超声导波
钢轨探伤
variational mode decomposition
particle swarm optimization
ultrasonic guided wave
rail flaw detection
