A Method of Quantitative Detection of Fatigue Crack Depth in Bottom Rails by Ultrasonic Guided Waves Based on PCA-SVM

Abstract

Ultrasonic guided wave is widely used to detect cracks in rail because of its long propagation distance and small attenuation. To effectively detect the fatigue crack in rail bottom through ultrasonic guided wave, an improved principal component analysis-support vector machine (PCA-SVM) intelligent algorithm based on grid search (GS) is proposed to detect the fatigue crack at different depths of rail bottom. The finite element method is used to establish the model of ultrasonic guided wave at different depths of the rail bottom, and the simulation model is compared with the experimental data to determine the effectiveness of the simulation model. Five main component features of the fatigue cracks at different depths are extracted by PCA. The GS method is used to optimize the penalty factor c and kernel function parameter g in the SVM, and the optimized SVM model is selected to identify the rail fatigue crack at different depths. The combination of theoretical simulation and experimental results shows that the accuracy of the training set and the test set of the improved PCA-SVM intelligent algorithm based on the GS method can reach 99.79 % and 99.73 %, respectively, which provides a basis and method for the detection of the fatigue crack depth of the rail bottom.