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    基于超声检测的主动式钢轨伤损智能识别方法

    Active intelligent identification method for steel rail damage based on ultrasonic testing

    • 摘要: 常规的主动式钢轨伤损智能识别方法,在钢轨伤损数据采集过程中耗时较多,使得伤损智能识别时间较长。为解决这一问题,提出一种基于超声检测的主动式钢轨伤损智能识别方法。首先按照增益范围,获取钢轨伤损回波信号,设置超声换能器的滤波器,对信号进行转换,从而对数据进行采集;然后根据DBSCAN算法,以伤损识别的最小单元为一个单元,对采集的超声数据进行组合而构成超声信息群,并对数据进行划分,按照划分数据对钢轨伤损特征进行匹配;最后以AlexNet网络架构为识别主体结构,以数据匹配结果为基础,建立显图样本数据集,对伤损类型进行精细搜索,从而得到主动式钢轨伤损智能识别结果。试验结果表明,所提方法对主动式钢轨伤损识别的时间较短,能实现对主动式钢轨伤损的快速识别,具有较好的应用价值。

       

      Abstract: The conventional active intelligent identification method for rail damage takes a long time in the process of collecting rail damage data, resulting in a longer intelligent identification time for rail damage. Therefore, an active intelligent identification method for rail damage based on ultrasound was proposed. Based on ultrasonic technology, the echo signal and rail damage echo signal were obtained according to the gain range, and the filter of ultrasonic transducer was set to convert the signal to collect data. According to the DBSCAN algorithm, the smallest unit for damage identification was used as a unit to combine the collected ultrasonic data to form an ultrasonic information group, and the data was divided. The rail damage features were matched according to the divided data. Finally, Using the AlexNet network architecture as the main identification structure, based on data matching results, a dataset of displayed samples was established to conduct a precise search for damage types, thus obtaining active intelligent identification results for rail damage. The experimental results showed that the method proposed in this paper had a shorter time for identifying active rail damage, and can achieve rapid identification of active rail damage, which has good application value.

       

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