【作者】 魏强；

【导师】 王艳；

【作者基本信息】 哈尔滨工业大学 ， 控制科学与工程， 2013， 硕士

【摘要】 随着列车运营速度的提高，人们也越来越关心列车的安全运行问题。很多铁路交通事故都是由于钢轨伤损造成的，因此，钢轨伤损检测方法的研究对列车的安全运行至关重要。目前主要是通过轨道检测车来对钢轨的伤损进行检测，一般是每隔几个月对线路检测一次，并且每次检测都要占用线路，随着铁路的高速化，这种检测方式也有一定的局限性。为了解决这个问题，本文提出了一种基于振动加速度传感器的钢轨伤损检测方法，通过在轨道沿线铺设振动加速度传感器，以无线的方式把数据传输到数据处理中心进行相应的处理，实现实时对钢轨的伤损进行检测。本文分别从理论和实验两个方面对钢轨伤损检测方法进行了研究。理论研究方面：首先通过数学方法建立了车辆-轨道垂向振动系统的模型，向系统中加入不同的钢轨伤损得出相应的伤损振动加速度信号。然后对这些信号进行滤波预处理后，分别从时域和时频域获取相应的特征参数，一起组成钢轨伤损的初始特征参数集。接着运用顺序后向选择法对该特征参数集进行特征选取。最后通过支持向量机对钢轨的伤损进行分类和识别。实验研究方面：设计了轮轨实验平台，包括轮轨接触系统，速度控制系统和数据采集系统。可以在该平台的模拟轨上造出一些典型的钢轨伤损，通过轮轨的接触，从而产生相应的钢轨伤损振动加速度信号。为了获得更多具有代表性的伤损信号，该平台还可以设定不同的模拟车速、不同的模拟车重和不同的测量位置。通过该实验平台获取一些典型的钢轨伤损信号，利用本文的方法对这些信号进行处理，然后进行分类和识别。实验的分类结果达到了预期的效果，说明了本文的钢轨伤损识别方法很有效。更多还原

【Abstract】 With the increase of the train operation speed, people are increasinglyconcerned about the safety of the train. Many railway traffic accidents are caused byrail defects, so the rail defect detection method are of great importance to the safetyof the train. Track inspection cars are the main rail defect detection tools at present,which detecting the rail every few months, and each time it will occupy the rail. Forhigh-speed railway, this detection method has some limitations. To solve thisproblem, a rail defect detection method base on acceleration sensors are proposed inthis paper. The acceleration sensors are distributed along the railway, from whichthe signals are sent to data processing center by wireless devices. Somecorresponding signal processing methods are used to attain real-time detection.An theoretical and experimental study of rail defect detection method waspresented in this paper.Theoretical research: Firstly, the vehicle-track vertical vibration system modelwas built by mathematic method. The corresponding vibration signals were acquiredby inputting the rail defects into the system. Secondly, after filtering preprocessingof the vibration signals, the corresponding feature parameters were acquired fromtime domain and time-frequency domain, with which the original rail defect featureparameter set was formed. Then the sequential backward selection method wasapplied to select the useful feature parameters. At last, the rail defects wereclassified and recognized by support vector machine.Experimental study: A wheel-rail experimental device was designed, which wasconsisted of wheel-rail contact system, speed control system and data acquisitionsystem. Some typical rail defects can be made on the simulation rail, and thecorresponding rail defect vibration signal was generated by wheel-rail contacting. Inorder to obtain more representative rail defect signals, the experimental device canset the simulation train speed, train weight and the measurement position. Sometypical rail defect signals were acquired by this experimental device. Then thesignals were processed by the method in this paper, and the rail defects are classifiedand recognized. The classification results of the experiments achieved theanticipated effects. The results show that the rail defect detection method in thispaper is very effective.更多还原