摘要
针对目前车辆脱轨多发生在道岔区段,且现有脱轨评判方式考虑因素单一,不能全面评判轮轨接触安全性的问题,提出一种基于多传感器数据融合的道岔区脱轨系数预测算法。通过T-snake模型对车载摄像机获取的9号道岔区轮轨接触图像进行图像分割得到轮轨相对横向位移量;选用遗传算法优化的小波神经网络构建融合模型,输入相对位移量,速度量,加速度量以及轮重减载率进行数据融合预测脱轨系数。现场测试结果显示,利用该方法的预测脱轨系数模型考虑因素较为全面,且具有良好的精确性和鲁棒性。
For the current vehicle derailment occurred in the switch section,and the existing way of derailing judge to consider a single factor can not fully judge the security issue of wheel-rail contact,an algorithm for predicting derailment coefficients in turnout areas based on multi-sensor data fusion is proposed.The method used the T-snake model to segment the wheel-rail contact image of the No.9 turnout area acquired by the vehicle camera to obtain the relative lateral displacement of the wheel-rail.The wavelet neural network optimized by genetic algorithm is used to construct the fusion model.The input data is the relative displacement amount,speed amount,acceleration amount,and wheel load reduction ratio to perform data fusion to predict the derailment coefficient.The field test results show the accuracy of the derailment coefficient predicted by this method is high,and the considerations are comprehensive.
作者
杨桐
董昱
YANG Tong;DONG Yu(College of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《铁道科学与工程学报》
CAS
CSCD
北大核心
2020年第8期1883-1892,共10页
Journal of Railway Science and Engineering
基金
国家自然科学基金资助项目(61763023)。
关键词
脱轨
铁路道岔
数据融合
脱轨系数预测
derailment
railway turnout
data fusion
derailment coefficient prediction
