基于互补集合经验模态分解和支持向量回归机的城市轨道交通线路轨距劣化预测
贾清天林海剑金忠
Gauge Deterioration Prediction of Urban Rail Transit Lines Based on CEEMD and SVR
JIA QingtianLIN HaijianJIN Zhong
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作者信息:上海地铁维护保障有限公司，200235，上海
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Affiliation:Shanghai Metro Maintenance Support Co., Ltd., 200235, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2025.01.009
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中图分类号/CLCN:U216.3
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栏目/Col:研究报告
摘要:
［目的］为了加强城市轨道交通区间线路质量的状态管理，需要对轨距在空间上的整体劣化趋势进行预测。［方法］引入CEEMD(互补集合经验模态)理论，提取轨道区间几何形位的IMF(本征模态函数)，利用PSO(改进粒子群)算法优化SVR(支持向量回归机)，对提取数据进行训练，标定预测模型最优参数后进行测试集验证，构建CEEMD-PSO-SVR预测模型。通过上海轨道交通16号线上行轨道区间K12+134—K15+743内的1128组轨检样本数据对预测模型进行了试验。［结果及结论］CEEMD-PSO-SVR预测模型同PSO-SVR模型、ARIMA(自回归移动平均模型)相比，在均方根误差、平均绝对误差、平均相对误差绝对值等3项性能评价指标上具有优势。
Abstracts:
［Objective］ In order to strengthen the status management of urban rail transit line sections, it is necessary to predict the overall deterioration trend of the gauge in space. ［Method］ CEEMD (complementary ensemble empirical mode decomposition) theory is introduced to extract the IMF (intrinsic mode function) of the geometric alignment of the track section. The PSO (particle swarm optimization) algorithm is utilized to optimize the SVR (support vector regression machine) to train and test the extracted data after calibrating the optimal parameters of the prediction model. Thus, the CEEMD-PSO-SVR prediction model is constructed. The prediction model is tested with 1,128 sets of track inspection sample data within the upward track section from K12+134 to K15+743 on Shanghai Metro Line 16. ［Result & Conclusion］ Compared with the PSO-SVR model and the ARIMA (autoregressive integrated moving average) model, the CEEMD-PSO-SVR prediction model has advantages in three performance evaluation indicators, namely root mean square error, mean absolute error, and absolute value of mean relative error.
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