摘要
钢轨表面缺陷的漏磁检测会受到巡检速度等因素的影响,导致背景噪声增大,检测灵敏度降低。为了增强缺陷信号特征,提高漏磁信号的信噪比,提出了一种基于最小熵解卷积的漏磁信号处理方法。通过目标函数法,计算得到最优的逆滤波器参数,对采集到的漏磁信号进行滤波处理。为衡量最小熵解卷积算法滤波效果,将处理得到的缺陷信号和背景噪声信号的峰峰值与小波变换法和中值滤波法进行对比。实验结果表明,最小熵解卷积算法对缺陷信号起到了明显的增强作用,且其效果优于小波变换和中值滤波。
The magnetic flux leakage detection of rail surface defects will be affected by the inspection speed and other factors,which increase the background noise and reduces the detection sensitivity.In order to enhance the defect signal characteristics and improve the signal-to-noise ratio of MFL signal,a MFL signal processing method based on minimum entropy deconvolution is proposed in this paper.Through the objective function method,the optimal inverse filter parameters are calculated,and the collected magnetic flux leakage signal is processed by filtering.In order to measure the filtering effect of the minimum entropy deconvolution algorithm,the pep-to-peak values of the processed defect signals and background noise signals were compared with the wavelet transform and median filtering.The experimental results show that the minimum entropy deconvolution algorithm plays a significant role in enhancing the weak defect signal,and its effect is better than that of wavelet transform and median filtering.
作者
朱玥
王平
张兆珩
贾银亮
Zhu Yue;Wang Ping;Zhang Zhaoheng;Jia Yinliang(School of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)
出处
《电子测量技术》
北大核心
2022年第17期167-170,共4页
Electronic Measurement Technology
基金
国家重点研发计划项目(2018YFB21009)资助。
关键词
最小熵解卷积
特征增强
缺陷识别
漏磁检测
minimum entropy deconvolution
feature enhancement
defect recognition
magnetic flux leakage detection
