Rail damage detection method based on acoustic emission and wavelet singularity

Abstract

很多重大脱轨事件都与钢轨的损伤密切相关,因此,对在役钢轨进行定期损伤检测显得尤为重要。研究首先运用声发射原理,通过对在役钢轨损伤前后监测系统所采集的数据信号进行时域及频域的分析,根据有损信号的能量谱特点,判断钢轨中损伤的存在。其次,运用小波奇异性检测原理探讨研究了定位损伤。通过分析各种连续小波变换算法的结果,得出à Trous算法在奇异性检测中能够较准确判断出奇异点位置。因此,结合声发射原理和小波处理的方法的无损监测可应用于在役钢轨的损伤检测和定位,对铁路钢轨损伤进行检测和预报。 Many major derail accidents are closely related to the rail damage, so the study on the rail flaw detection technology is particularly important nowadays. The study is aiming at analyzing and comparing the characteristics of signals data before and after destruction, collected by a damage detection system. The damage and defect were judged by the differences between the processed data of destructive signals and nondestructive ones in time and frequency domain and according to the energy spectrum features. What's more, the locations of defects and damages were obtained by virtue of the singularities of destructive signals using three wavelet singularity analysis methods, including continuous wavelet transform, Mallat algorithm and à Trous algorithm. It is found that à Trous algorithm can give quite accurate information about real damage locations, which shows that this method can be used in the real damage detection for rails and provide us more precise defect location information.