关键词: 超声成像, 高速轨道, 伤损检测, 参数学习, 决策模型

Abstract: Considering the problem of high-speed rail damage detection,a damage detection method was proposed based on 0°,37° and 70° ultrasonic probes.Based on B-scan image of damage,the color,area,tilt angle,length,and center of mass coordinates were analyzed in the proposed method,and the damage detection algorithm was designed based on their logical relationships of damage features.Furthermore,the image features of the same damage type usually show obviously differences due to uncertainty disturbance factors in the ultrasonic imaging process,which will affect its detection accuracy.For these reasons,a parameter learning method was proposed to improve the detection accuracy of the proposed algorithm,which could adjust the thresholds of the detection parameters in time.Firstly the detection parameters were extracted based on the established detection algorithm model.Secondly combining with the key detection parameters of damage,a learning model based on support vector machine was established to maximize the class feature interval of contours in the same region under the same feature constraints,and the parameters threshold was optimized and adjusted based on the model.The experimental results showed that the accuracy of rail damage detection could reach 97.5% by using the proposed method,and the accuracy could be significantly improved by learning and re-detecting the damages with low detection rate in the initial inspection.The experimental results verified the effectiveness of the proposed method.

Key words: ultrasonic imaging, high-speed track, damage detection, parameter learning, decision model