發(fā)布時(shí)間：2018-07-03 12:18

  本文選題：探地雷達(dá) + 有砟鐵路�。� 參考：《北京交通大學(xué)》2016年博士論文

【摘要】：目前我國鐵路總里程突破11.2萬km,有砟鐵路(包含橋梁和隧道)總里程近9.6萬km。針對(duì)有砟鐵路的里程數(shù)和狀態(tài)檢測(cè)要求,探地雷達(dá)技術(shù)成為當(dāng)今連續(xù)檢測(cè)有砟鐵路路基的主要技術(shù)手段。然而,探地雷達(dá)檢測(cè)數(shù)據(jù)量大,處理識(shí)別時(shí)間延遲,致使數(shù)據(jù)解釋和病害識(shí)別存在主觀性強(qiáng)、判別標(biāo)準(zhǔn)不一、識(shí)別精度低等諸多問題,因此,亟需開展有砟鐵路路基病害的雷達(dá)圖像識(shí)別方法研究,實(shí)現(xiàn)路基病害的快速、準(zhǔn)確識(shí)別,為病害的整治處理提供技術(shù)支持。本文針對(duì)鐵路路基病害識(shí)別過程中面臨的判別標(biāo)準(zhǔn)不一、時(shí)效性差、識(shí)別精度低等諸多問題,依托國家"863"計(jì)劃項(xiàng)目(2009AA11Z212)和河北省應(yīng)用基礎(chǔ)研究重點(diǎn)資助研究項(xiàng)目(11963544D),以大秦鐵路的部分路段為研究對(duì)象,通過實(shí)際檢測(cè)數(shù)據(jù)和理論分析的研究,建立了基于雷達(dá)圖像特征的典型路基病害分類方法,分析了典型路基病害(缺陷)的發(fā)生機(jī)理、發(fā)育特征和空間分布特性,進(jìn)而研究了典型路基病害的參數(shù)表征以及識(shí)別方法,最終形成了基于探地雷達(dá)技術(shù)的鐵路路基病害快速識(shí)別方法。本文主要研究成果和結(jié)論如下：1、在充分認(rèn)識(shí)現(xiàn)行路基病害分類方法的基礎(chǔ)上,分析了典型路基病害的表征現(xiàn)象、基本成因和形狀特征,并結(jié)合探地雷達(dá)技術(shù)的檢測(cè)特點(diǎn)和鐵路路基在雷達(dá)圖像的表征形式,建立了基于雷達(dá)信號(hào)特征的路基病害分類方法,為路基病害的特征提取以及識(shí)別技術(shù)提供前期基礎(chǔ)。2、基于多條運(yùn)營線路路基的雷達(dá)實(shí)測(cè)數(shù)據(jù),分析了典型路基病害的發(fā)生機(jī)理及空間分布,得出了相應(yīng)病害在長(zhǎng)度及深度方向上的統(tǒng)計(jì)分布規(guī)律,為后續(xù)的病害特征提取及隨機(jī)匹配提供了必要的先驗(yàn)信息支持。根據(jù)鐵路路基檢測(cè)技術(shù)指標(biāo)以及探地雷達(dá)的技術(shù)參數(shù),設(shè)計(jì)了滿足路基檢測(cè)要求的探地雷達(dá)工作參數(shù)及雷達(dá)天線配置,并將系統(tǒng)安裝于軌道檢查車上,采用頻率為100M和400M雷達(dá)天線,同時(shí)檢測(cè)不同深度的路基狀態(tài),完成了大秦線部分路段路基的檢測(cè)。3、本文從時(shí)域、時(shí)頻域和物理幾何稀疏性等方面對(duì)典型路基病害雷達(dá)圖像的特征提取進(jìn)行研究,并建立了最優(yōu)的雷達(dá)信號(hào)特征表示。一種是通過主成分分析確定了以分塊能量、分層點(diǎn)頻率和能量均值作為鐵路路基的時(shí)域特征值,獲得了較低維的空間特征：一種是對(duì)典型路基病害雷達(dá)圖像的距離-深度二維時(shí)頻特征表現(xiàn)進(jìn)行了分析,獲取了路基雷達(dá)檢測(cè)圖像的低頻信息和小波多尺度空間細(xì)節(jié)能量譜特征,從而減少了采樣數(shù)據(jù)量；一種采用稀疏表示的方法提取雷達(dá)信號(hào)特征向量,采用投影變換得到了路基病害的垂直和水平投影線段,并以此作為判斷路基病害類型的依據(jù),從而為圖像的特征提取提供了理論依據(jù)。4、針對(duì)反映路基雷達(dá)圖像同相軸延續(xù)性和紊亂程度的時(shí)域特征存在的冗余性和局限性問題,通過主成分分析確定了低維時(shí)域特征值。同時(shí)在所確定時(shí)域特征的基礎(chǔ)上,分別采用了人工神經(jīng)網(wǎng)絡(luò)、廣義神經(jīng)網(wǎng)絡(luò)聚類算法和支持向量機(jī)等人工智能的方法對(duì)路基病害進(jìn)行識(shí)別并加以比較,最終確定了以支持向量機(jī)作為路基病害的有效識(shí)別算法,從而為鐵路路基及其典型病害的識(shí)別奠定了算法基礎(chǔ)。5、利用路基病害的物理幾何特點(diǎn),通過提取有效的雷達(dá)圖像的邊界曲線,得到了路基病害的垂直和水平投影線段,并將垂直投影線段和能量譜作為路基病害的有效特征,最終形成了基于該特征的鐵路路基病害的識(shí)別方法。分析結(jié)果表明：數(shù)據(jù)的采集量?jī)H為原數(shù)據(jù)量的1/4,識(shí)別速度提高將近4倍,并提出了HS-SVM方法識(shí)別路基病害,且其識(shí)別率在85%以上,實(shí)現(xiàn)了路基病害的快速識(shí)別。
[Abstract]:At present, the total mileage of railway in China breaks through 112 thousand km, and the total mileage of ballasted Railway (including bridges and tunnels) is nearly 96 thousand km. for the mileage and state detection requirements of ballasted railway. Ground penetrating radar technology has become the main technical means for continuous detection of ballastballed railway subgrade. However, the detection data of the ground penetrating radar is large and the time of processing recognition is delayed. There are many problems in data interpretation and disease identification, such as strong subjectivity, different criteria and low recognition accuracy. Therefore, it is urgent to carry out the research of radar image recognition method for the disease of ballastballed railway subgrade, to realize the rapid and accurate recognition of subgrade diseases, and to provide technical support for the treatment of disease. According to the different criteria of discrimination, poor timeliness and low recognition accuracy, we rely on the national "863" project (2009AA11Z212) and the key support research project (11963544D) of the applied basic research in Hebei Province, and take the section of the railway of the Daqin Railway as the research object, and establish the basis of the research on the actual testing data and theoretical analysis. The typical subgrade disease classification method of radar image features analyses the occurrence mechanism, development characteristics and spatial distribution characteristics of typical subgrade disease (defect), and then studies the parameter characterization and recognition method of typical subgrade diseases, and finally forms a rapid identification method of railway subgrade disease based on ground penetrating radar technology. The results and conclusions are as follows: 1, on the basis of fully understanding the existing classification methods of subgrade disease, the characteristics of typical subgrade diseases, basic causes and shape characteristics are analyzed, and the detection characteristics of ground penetrating radar technology and the characteristic form of the railway subgrade in radar image are combined, and the subgrade disease based on radar signal features is established. This method provides the early foundation.2 for the feature extraction and recognition technology of subgrade diseases. Based on the radar data from the subgrade of multiple operating lines, the occurrence mechanism and spatial distribution of typical subgrade diseases are analyzed, and the statistical distribution rules of the corresponding diseases in the direction of length and depth are obtained, and the characteristics of the disease are extracted and random. The matching provides the necessary prior information support. According to the technical parameters of the railway subgrade detection and the technical parameters of the ground penetrating radar, the working parameters of ground penetrating radar and the configuration of the radar antenna are designed to meet the requirements of the subgrade detection, and the system is installed on the track inspection car, the frequency is 100M and 400M radar antenna, and the different depth is detected at the same time. The subgrade condition of the subgrade of the Daqin line is tested.3. This paper studies the feature extraction of the typical subgrade disease radar image from the time domain, time and frequency domain and the physical geometric sparsity, and establishes the optimal radar signal feature representation. One is to determine the block energy and the stratified point frequency by the principal component analysis. As the time domain characteristic value of the railway subgrade, the mean value of the energy is obtained, and the spatial characteristics of the lower dimension are obtained. One is the analysis of the feature of the distance to depth two-dimensional time-frequency characteristic of the typical subgrade disease radar image, and the low frequency information of the subgrade radar detection image and the feature of the wavelet multi scale spatial detail energy spectrum are obtained, thus reducing the mining of the subgrade. A method of sparse representation is used to extract the characteristic vector of radar signal, and the vertical and horizontal projection lines of subgrade disease are obtained by projection transformation, which is used as the basis for judging the type of subgrade disease, thus providing a theoretical basis for the feature extraction of the image, which reflects the continuity of the same phase axis of the subgrade radar image. At the same time, artificial neural network, generalized neural network clustering algorithm and support vector machine such as artificial neural network, generalized neural network clustering algorithm and support vector machine are used to identify the subgrade disease on the basis of the time domain characteristics. By comparing it, the effective recognition algorithm of support vector machine as subgrade disease is determined and the algorithm foundation.5 is laid for the identification of railway subgrade and its typical diseases. By using the physical geometric characteristics of the subgrade disease, the vertical and horizontal projection lines of the subgrade disease are obtained by extracting the effective boundary curve of the radar image. With the vertical projection line and the energy spectrum as the effective characteristics of the subgrade disease, the identification method of railway subgrade disease based on this feature is formed. The analysis results show that the data collection is only 1/4 of the original data, the recognition speed is nearly 4 times higher, and the HS-SVM method is proposed to identify the subgrade disease, and its recognition rate is 85% Above, the rapid identification of subgrade disease has been realized.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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