高鐵接觸網(wǎng)旋轉(zhuǎn)雙耳銷釘狀態(tài)檢測方法研究
發(fā)布時間：2018-08-01 17:24
【摘要】：針對高速鐵路接觸網(wǎng)支撐裝置旋轉(zhuǎn)雙耳部件銷釘?shù)乃擅撆c脫落問題,提出一種基于圖像不變性目標定位及灰度分布規(guī)律特征的銷釘不良狀態(tài)檢測方法。通過分析現(xiàn)場接觸網(wǎng)支撐及懸掛裝置圖像,利用SIFT(Scale Invariant Feature transform)算法和改進的RANSAC(Random Sample Consensus)算法實現(xiàn)雙耳部件的定位;采用Hough變換實現(xiàn)目標圖像中雙耳套筒傾角的提取,并將其旋轉(zhuǎn)至水平方向,進而實現(xiàn)旋轉(zhuǎn)雙耳部分的分割;累加目標圖像的豎直方向像素灰度值,確定銷釘受力部分和兩端非受力部分長度;歸納銷釘正常工作及故障時這些長度間相關(guān)比值的范圍,從而判斷銷釘?shù)墓ぷ鳡顟B(tài)。實驗表明,該方法能夠較準確地實現(xiàn)銷釘不良狀態(tài)的檢測。
[Abstract]:In order to solve the problem of loosening and shedding of pin of rotary binaural parts in catenary support device of high-speed railway, a new method for detecting the bad state of pin based on image invariance target location and gray distribution rule is proposed. By analyzing the images of field catenary support and suspension device, the location of binaural parts is realized by using SIFT (Scale Invariant Feature transform) algorithm and improved RANSAC (Random Sample Consensus) algorithm, and the obliquity of binaural sleeve in target image is extracted by Hough transform. It rotates to the horizontal direction, then realizes the segmentation of the rotating binaural part, accumulates the pixel gray value in the vertical direction of the target image, and determines the length of the pin bearing part and the unloaded part at both ends. The range of the correlation ratio between these lengths when the pin is in normal operation and fault is summarized to judge the working state of the pin. Experiments show that the method can accurately detect the bad state of pin.
【作者單位】： 西南交通大學(xué)電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金(U1434203,51377136,51407147)
【分類號】：TP391.41;U226.8
本文編號：2158298
[Abstract]:In order to solve the problem of loosening and shedding of pin of rotary binaural parts in catenary support device of high-speed railway, a new method for detecting the bad state of pin based on image invariance target location and gray distribution rule is proposed. By analyzing the images of field catenary support and suspension device, the location of binaural parts is realized by using SIFT (Scale Invariant Feature transform) algorithm and improved RANSAC (Random Sample Consensus) algorithm, and the obliquity of binaural sleeve in target image is extracted by Hough transform. It rotates to the horizontal direction, then realizes the segmentation of the rotating binaural part, accumulates the pixel gray value in the vertical direction of the target image, and determines the length of the pin bearing part and the unloaded part at both ends. The range of the correlation ratio between these lengths when the pin is in normal operation and fault is summarized to judge the working state of the pin. Experiments show that the method can accurately detect the bad state of pin.
【作者單位】： 西南交通大學(xué)電氣工程學(xué)院;
【基金】：國家自然科學(xué)基金(U1434203,51377136,51407147)
【分類號】：TP391.41;U226.8
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