Rail damage detection method based on acoustic emission and wavelet singularity

All right reserved. 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.