钢轨探伤车综合智能检测系统

大型钢轨探伤车普遍采用超声波检测钢轨内部疲劳伤损,但国内已有的超声波系统架构平台在复杂线路区间探伤检测运用时存在数据拥塞和计算机死机现象而导致区段漏检,并且伤损的识别主要依靠人工全程回放。为提高信号处理速度和伤损识别能力,降低人工回放的工作量,进行了基于新型总线的超声波探伤系统和基于卷积神经网络深度学习的伤损智能识别技术研究;为提高钢轨表面和近表面的伤损检出能力,开展了钢轨表面图像检测技术研究;为实现各检测系统数据同步采集和同步回放,综合判定钢轨健康状况,设计了空间同步定位系统。经验证和对比,在平均伤损误报率基本相当的前提下,采用了深度学习算法的系统的平均人工伤损检出率比既有系统提高了4%以上;各系统之间同步误差在1 m以内,伤损实际复核的定位精度在3 m以内。

Integrated Intelligent System for Rail Flaw Detection Vehicle

Ultrasonic testing is widely used to detect rail internal fatigue damage in large-scale rail flaw detection vehicles. However, the existing domestic ultrasonic system architecture platform has the phenomenon of data congestion and computer crash when it is used in the detection of complex railway sections, which leads to the missing detection of sections. Moreover, the identification of the damage mainly depends on the whole manual playback. In order to improve the signal processing speed and damage identification ability, and reduce the workload of manual playback, ultrasonic flaw detection system based on new bus and intelligent damage identification technology based on convolution neural network deep learning were carried out. In order to improve the detection ability of rail surface and near surface damage, the research on rail surface image detection technology was carried out. In order to realize the synchronous data acquisition and playback of each detection system, and comprehensively determine the rail health status, a spatial synchronous positioning system was designed. After verification and comparison, on the premise that the average false alarm rate of injury was basically the same, the average artificial damage detection rate of the system using the deep learning algorithm was increased by more than 4% compared with the existing system;the synchronization error between the systems was within 1 m, and the positioning accuracy of the actual review of the injury was within 3 m.