摘要
不同于现有检测方法,利用CA砂浆脱空和非脱空情况下采集到的声音信号不同,提出把机器学习领域中的GBRT算法应用到CA砂浆脱空检测领域。利用脱空和非脱空情况下采集到的数据,使用GBRT算法训练出一个二分类模型;通过对输入数据做类别决策,判断对应的采集位置是否脱空。介绍算法原理,并结合CA砂浆脱空问题,分析模型的使用技巧;引入其他主流机器学习分类算法进行比较分析。实验结果证明,GBRT算法构造的模型性能较好,在CA砂浆脱空检测领域具有应用前景。
Different from the existing detection methods, the GBRT algorithm of machine learning was applied in the CA mortar disengaging detection field using the difference between the sound signal of track slab collected at void and non-void situation for the first time. Using the data collected at the void and non-void situation, a binary classification model was trained with the GBRT algorithm. Category-decision was made for the input data and whether corresponding gathering place is void was judged. The algorithm principle was explained in detail, and the use skills of the model were analyzed combined with the CA mortar disengaging detection. Other mainstream machine learning classification algorithms were introduced for comparative analysis. And the result shows that the GBRT algorithm is much better and has a great application prospect in this field.
出处
《铁道科学与工程学报》
CAS
CSCD
北大核心
2018年第2期292-301,共10页
Journal of Railway Science and Engineering
基金
国家自然科学基金资助项目(61271383)
华侨大学研究生科研创新能力培育计划资助项目(1511314017)
关键词
无砟轨道
CA砂浆
脱空
GBRT算法
机器学习分类算法
ballastless track
CA mortar
disengaging
GBRT algorithm
machine learning classification algorithm
作者简介
通信作者：谢维波（1964-）,男,福建泉州人,教授,从事机器学习算法及其应用研究;E-mail：xwblxf@hqu.edu.cn
