基于最小熵反褶积的钢轨表面缺陷漏磁检测信号滤波方法

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Swarm Intelligence Research Pub Date : 2023-10-25 DOI:10.4018/ijsir.332791
Jing Liu, Shoubao Su, Haifeng Guo, Yuhua Lu, Yuexia Chen
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引用次数: 0

摘要

轨道表面缺陷漏磁检测是铁路交通安全的一个重要研究领域。由于磁化和材料等因素,会产生背景噪声,降低检测精度。为了提高检测信号强度,提高对较小缺陷的检出率,提出了一种基于最小熵反褶积的信号滤波方法。采用目标函数法,计算出最优反滤波参数,并将其应用于轨道表面漏磁信号的滤波检测。检测结果表明,该算法检测到的缺陷信号与噪声信号的峰峰比为2.01,是小波变换方法和中值滤波方法的1.5倍左右。缺陷信号明显增强,能有效提高钢轨表面微小缺陷的检出率。
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A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution
Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.
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来源期刊
International Journal of Swarm Intelligence Research
International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
2.50
自引率
0.00%
发文量
76
期刊介绍: The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.
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