【摘要】：鋼繩芯輸送帶內(nèi)嵌鋼絲繩芯,外覆膠質(zhì)帶體,廣泛地應(yīng)用于煤礦、港口、碼頭等領(lǐng)域的物料運輸。但由于輸送帶的應(yīng)用環(huán)境大都較惡劣,加之長期負重運行,輸送帶內(nèi)的鋼絲繩芯極易發(fā)生接頭抽動、繩芯斷裂等損傷,給工業(yè)生產(chǎn)帶來嚴重的事故隱患。因此,對輸送帶的鋼芯狀況進行實時檢測具有重大意義。目前,常用的輸送帶實時檢測方法包括基于電磁感應(yīng)原理的漏磁檢測法和基于X射線成像的圖像分析法。漏磁檢測法只能獲取鋼芯損傷的大概程度和大致位置,檢測結(jié)果易受干擾;而基于X射線成像的檢測方法可直觀獲得輸送帶內(nèi)部的鋼繩狀況,且便于使用圖像處理方法對其進行分析判別,因此該方法正成為目前的研究方向。本文正是基于輸送帶的X射線圖像,對鋼繩的缺陷檢測方法進行了研究,主要工作內(nèi)容有:(1)輸送帶X射線圖像增強處理。受工作環(huán)境的影響,直接采集到的輸送帶X射線圖像往往具有較多的干擾噪聲,且在成像時輸送帶兩側(cè)由于距離X射線源較遠而獲得較少的射線能量,使得采集到的兩側(cè)圖像整體較暗,鋼芯和膠質(zhì)的對比度不明顯。針對這種情況,本文通過比較各種去噪方法,選取合適的濾波器對圖像進行噪聲抑制,同時,根據(jù)輸送帶圖像的像素分布特征,提出一種改進的Retinex圖像增強算法,實現(xiàn)了圖像的對比度增強,便于后續(xù)的接頭定位和接頭抽動檢測。(2)輸送帶鋼繩接頭抽動識別。針對現(xiàn)有的鋼繩芯接頭抽動檢測方法大多是基于接頭點對的匹配,通過匹配點對間的垂直距離和接頭標準距離或參考圖像的點對垂直距離作比較來檢測接頭抽動的情況,本文將對這種方法存在的不足進行改進,即實現(xiàn)圖像反拉伸后的完全抽動檢測。實驗表明,該方法可實現(xiàn)對所有接頭的檢測,使檢測結(jié)果更加全面、準確。(3)輸送帶鋼繩芯斷裂識別。對輸送帶非接頭部位的X射線圖像進行研究,考察該部位圖像的紋理特性,分析各紋理缺陷檢測算法對鋼繩斷裂檢測的效果,并在此基礎(chǔ)上提出一種基于紋理規(guī)則性的鋼繩斷裂檢測方法,實驗表明,該方法能夠有效地檢測出輸送帶中斷裂的鋼芯位置,鋼芯斷裂檢測精度達到了98.9%。
[Abstract]:Steel rope core conveyor belt is embedded with steel rope core and covered with colloidal belt body, which is widely used in coal mine, port, wharf and other fields of material transport. However, due to the harsh application environment of the conveyor belt, coupled with long-term load operation, the steel rope core in the conveyor belt is very easy to occur joint twitching, core fracture and other damage, which brings serious industrial production. At present, the commonly used real-time detection methods of conveyor belts include magnetic flux leakage detection based on electromagnetic induction principle and image analysis based on X-ray imaging. Magnetic flux leakage detection method can only obtain the approximate degree and location of steel core damage, and the detection results. The method based on X-ray imaging is easy to be interfered with, and the wire rope condition inside the conveyor belt can be obtained intuitively, and it is easy to be analyzed and discriminated by image processing method, so this method is becoming the current research direction. The main contents are as follows: (1) X-ray image enhancement processing of conveyor belt. Under the influence of working environment, the conveyor belt X-ray images collected directly often have more interference noise, and in imaging, the conveyor belt has less radiation energy because of the distance from the X-ray source on both sides of the conveyor belt. In view of this situation, by comparing various denoising methods, this paper chooses the appropriate filter to suppress the noise of the image. At the same time, according to the pixel distribution characteristics of the conveyor belt image, an improved Retinex image enhancement algorithm is proposed, which realizes the image contrast enhancement and facilitates the subsequent joint location and connection. Twist detection. (2) Twist identification of steel rope joints in conveyor belts. Most of the existing methods of Twist detection of steel rope core joints are based on the matching of joint point pairs. By comparing the vertical distance between matching points and the standard distance between matching points and the vertical distance between matching points or the point pairs of reference images, this method will exist in this paper. The experimental results show that the method can detect all the joints more comprehensively and accurately. (3) Fracture identification of steel rope core of conveyor belt. X-ray image of non-joint part of conveyor belt is studied, texture characteristics of this part of image are investigated, and each part is analyzed. Based on the effect of texture defect detection algorithm on wire rope fracture detection, a wire rope fracture detection method based on texture regularity is proposed. The experimental results show that the method can effectively detect the position of steel core of conveyor belt fracture, and the detection accuracy of steel core fracture reaches 98.9%.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TH222;TP391.41

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