基于数据驱动的高速铁路动车组列车头尾车车轮踏面缺陷预测方法
王文琦1宋冬利1李林2刘翊2张卫华1郑则君1
Data-driven Prediction Method for High-speed Railway EMU Train Front and Rear Car Wheel Tread Defects
WANG Wenqi1SONG Dongli1LI Lin2, LIU Yi2ZHANG Weihua1ZHENG Zejun1
-
作者信息:1.西南交通大学轨道交通运载系统全国重点实验室， 610031， 成都
2.株洲国创轨道科技有限公司， 412007， 株洲
-
Affiliation:1.State Key Laboratory of Rail Transit Vehicle System, Southwest Jiaotong University, 610031, Chengdu, China
2.Zhuzhou Guochuang Track Technology Co., Ltd., 412007, Zhuzhou, China
-
关键词:
-
Key words:
-
DOI:10.16037/j.1007-869x.2024.04.006
-
中图分类号/CLCN:U269.6
-
栏目/Col:学术专论
摘要:
［目的］踏面缺陷是高速铁路动车组列车车轮失效的主要表现形式之一，严重影响了动车组运行的安全性及乘客的乘坐舒适性。车轮踏面缺陷主要集中在头尾车，可能是多种因素共同作用的结果，需要寻找一种能综合多种影响因素的预测方法。［方法］基于某铁路局动车组列车的车轮镟修数据，每个数据样本包括10个特征（4个名义特征和6个连续特征），对数据进行预处理。通过合成少数类样本过采样技术对不平衡数据集进行处理，构建了标准化数据集。建立了DNN（深度神经网络）模型，对底层特征进行组合，形成了特征的高层抽象表示。通过网络结构调整和超参数优化得到了模型的最优学习效果。对模型进行训练并测试，验证了该模型的预测效果。［结果及结论］基于数据驱动的头尾车车轮踏面缺陷预测方法具有较高的预测精度和较优的综合性能，其预测精确率达92.5%，可有效预测头尾车车轮踏面损伤的发生概率。
Abstracts:
［Objective］ Tread defects are a primary manifestation of wheel failures in high-speed railway EMU (electrical multiple unites) trains, significantly impacting both EMU train operation safety and passenger ride comfort. Wheel tread defects are predominantly concentrated on front and rear cars, which may result from a combination of various factors, requiring predictive method that comprehensively integrate various influencing factors. ［Method］ Based on the wheel reprofiling maintenance data of EMU train operated by a railway bureau, the dataset sample consists of 10 features (including 4 nominal and 6 continuous features) and the data are preprocessed. By treating the imbalanced dataset through synthetic minority over-sampling technique (SMOTE), a standardized dataset is constructed. A DNN (deep neural network) model is established to combine the underlying features and form a high-level abstract representation of the features. The optimal learning performance of the model is achieved through network structure adjustment and hyperparameter optimization. The model is trained and tested to verify its prediction effect. ［Result & Conclusion］ The data-driven prediction method for wheel tread defects of front and rear cars demonstrates high-predictive accuracy and relatively excellent comprehensive performance, achieving a precision rate of 92.5%. Thus the probability of wheel tread damage of front and rear cars can be effectively predicted.
- 上一篇: 高铁物流运输模式及其可行性
- 下一篇: 打造系列化、智能化市域列车平台
过刊浏览
资料下载
友情链接