Adaptive Filtering Method of MFL Signal on Rail Top Surface Defect Detection

Abstract

Magnetic flux leakage (MFL) detection technology provides an effective method to conduct high-speed detection of the damage suffered by rail surface. With regard to high-speed detection, there is frequently a complex noise contained in the magnetic signal of railway leakage, which is similar to the amplitude of defect and the overlaps of frequency spectrum. In this paper, an improved adaptive filtering method is proposed to solve the problem caused by filtering the MFL signal on the rail top surface. Through the characteristics of distribution shown by defects on the top surface of the railway and those of the data collected by the rail top array sensor, this method is applied to construct a virtual channel containing almost only interference signals but no defects. Then, in combination with the adaptive filtering algorithm, the virtual channel signal is taken as the reference input of the adaptive canceller, each single channel MFL signal is taken as the original input of the adaptive canceller, and the filtered MFL signal is taken as the output. Then, the MFL signal of rail top is collected by the train at the speed of 30km / h on the manual calibration line. According to the experimental results, the noise intensity of MFL signal is reduced by up to 81.44%. In addition, the filtering method is adopted to process MFL signals with different directions and varying detection speed. As indicated by the results, the noise intensity of MFL signal is reduced by more than 74%. INDEX TERMS High speed rail detection; Magnetic flux leakage signal; Adaptive filtering algorithm; Reference signal; Adaptive noise canceller. KAILUN JI received the B.Eng. and the M.S. degree in techniques and instruments of measurement from the Nanjing University of Aeronautics and Astronautics, China, in 2016 and 2020. He is currently pursuing the Ph.D. degree at the Nanjing University of Aeronautics and Astronautics of China. His research interests include nondestructive testing, signal processing and system modeling.

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