铁路轨道不平顺状态的预测及其应用研究

摘要

伴随着“一带一路”战略的实施，铁路作为中国经济运行的大动脉，将成为推动“一带一路”战略实施的重要工具。保障铁路运行安全是铁路运输正常运营的重要前提，也是铁路相关部门的工作核心。以轨道不平顺检测数据为研究对象，对轨道状态恶化规律进行挖掘，有助于铁路相关部门科学合理地编排轨道养护维修的计划，从而确保列车运营安全。

首先，在轨道检测数据的预处理阶段，论文针对实测数据中两个主要的质量问题：离群点和里程漂移，分别采用绝对均值修正法和基于趋势相似性的数据偏移校正算法对其进行预处理。实验结果表明，预处理方法能够准确识别并修正异常值，并对里程漂移的数据进行相对校准，为接下来的预测工作提供可靠的数据支撑。

其次，在轨道状态预测分析阶段，论文分别建立轨道局部不平顺模型和轨道区段不平顺模型，以满足铁路工务部门对线路精细化管理和宏观调控的要求。一方面，论文针对小样本和随机波动性大的轨道几何不平顺时间序列的预测问题，借助句法模式识别理论的优势，研究并建立基于回归自动机的轨道局部质量状态预测模型。将相邻轨道具有相似性趋势变化的时间序列整合为轨道不平顺时空数据集合，并作为本文的数据研究对象。通过充分挖掘相邻轨道不平顺的空间信息以弥补其在时间序列上样本缺乏的不足。由于回归自动机能够较好地处理轨道系统内部各种不确定性因素的影响，因此，本文通过建立回归自动机轨道质量预测模型，为随机波动性较大的轨检数据预测问题提供解决方案。另一方面，论文借助小波分析能较好地处理非平稳信号的优势，将其应用到非平稳性轨道区段不平顺时间序列的预测问题。首先对非平稳原始序列进行小波分解后形成若干平稳序列；然后分别为各平稳序列选择合适的预测模型；最后通过小波重构方法完成原始轨道区段不平顺时间序列的预测任务。实验结果表明，本文提出的两种轨道不平顺预测模型在能够有针对性地解决某种问题的基础上，具有较高的拟合与预测精度。

最后，在辅助制定养护维修计划的阶段，论文基于轨道质量状态预测模型的研究成果，围绕着“是否维修”、“什么时候修”、“在哪里修”以及“怎样修”等关键性问题，分别对普通区段和薄弱区段的预防性养护维修计划的制定过程进行详细阐述和实例分析。

关键词：轨道不平顺；轨道局部波动指数；回归自动机；轨道质量指数；小波分解重构

Abstract

With the implementation of"One Belt One Road Initiative",as the artery of economy of China,the railway will become an important tool promoting the goal.How to ensure the safety of railway transportation is an essential prerequisite for railway operation and the core work of relevant railway departments.In order to meet the prerequisite,the maintenance plan of railway track should be made under the help of studying on track irregularity data and mining the development trend of track state.

Focusing on the following two quality problems of track irregularity data in the first place:the outlier and the mileage drift,the absolute mean value method and the data variation calibration algorithm based on trends similarity are utilized to solve them respectively in the preprocessing stage.The result shows that the outliers can be identified and corrected accurately as well as the mileage drift can be calibrated relatively,which can contribute to providing reliable data for supporting later prediction research.

Furthermore,for meeting requirements about delicacy management and macro-control of railway departments,a local irregularity prediction model and section irregularity prediction model are established during the prediction of track state.On the one side, considering two features of track irregularity data in time dimension:smallness of sample and stochastic fluctuation,the prediction model of track quality state with Regression Automata(RA)has been established based on the theory of syntactic pattern recognition.The drawback of lacking track irregularity samples in time dimension can be compensated by assembling space information of adjacent tracks.In this thesis,RA model which can handle the effect of uncertainty in track system is applied to solve the problem of random variation. On the other side,taking advantage of wavelet transform,it is used to solve the prediction problem about non-stationary time series.Firstly,a non-stationary time series of track irregularity can be decomposed into several stationary signals by means of wavelet transform. Furthermore,optimal fitting model is sought for each stationary signal.At last,the final prediction result of non-stationary time series can be received by recomposing the prediction data of all stationary signals.The experiment result shows that the two models proposed in this thesis both have higher prediction accuracy compared with GM(1,1)model.

Finally,the experimental results of track state prediction models mentioned above are used to assist relevant railway departments in maintenance planning,which can handle questions like"Whether","When","Where"and"How"about repairing.A few examples are

used to explain the procedure of drawing up preventive maintenance plan for normal track and the weak track respectively.

Key words:track irregularity,track local fluctuation index,regression automata,track quality index,wavelet decomposition and reconstruction.