【摘要】：軸承是機(jī)械行業(yè)中非常重要且應(yīng)用十分廣泛的轉(zhuǎn)動(dòng)部件，其生產(chǎn)批量大，精度要求高。在軸承的生產(chǎn)和使用中，為了保證軸承產(chǎn)品生產(chǎn)和使用正常，需對(duì)半成品或者成品軸承進(jìn)行檢測(cè)。目前大多數(shù)軸承生產(chǎn)廠家采用接觸式檢查方法即人工檢測(cè)方法。該方法檢測(cè)速度不僅慢，而且檢測(cè)者主觀因素會(huì)影響檢測(cè)結(jié)果，影響工件質(zhì)量，尤其是表面質(zhì)量，，在大規(guī)模的自動(dòng)化生產(chǎn)中存在弊端，對(duì)處于工作環(huán)境中軸承部件的檢測(cè)也同樣不利。 針對(duì)上述問(wèn)題，本文旨在研究一種非接觸式的軸承品質(zhì)檢測(cè)方法，即基于圖像理論的軸承品質(zhì)檢測(cè)，這種方法不僅避免了接觸式檢測(cè)的弊端，而且由于其能自動(dòng)檢測(cè)，非人工干預(yù)，具有高速、高精度、自動(dòng)等特點(diǎn)，符合當(dāng)今社會(huì)大生產(chǎn)的需求。 目前，已有的一些基于產(chǎn)品圖像缺陷檢測(cè)方法概括起來(lái)，有兩大類：第一類是基于圖像產(chǎn)品的灰度信息判斷產(chǎn)品的好壞，這類方法簡(jiǎn)單的用單閾值法將產(chǎn)品與缺陷信息分割，但可能會(huì)丟失部分缺陷信息；第二類是基于圖像產(chǎn)品的紋理信息判斷產(chǎn)品的好壞，這類方法在檢測(cè)速度和對(duì)缺陷信息聚類方面存在一些不足。 結(jié)合軸承圖像自身特點(diǎn)，本文利用最小二乘法和軸承相關(guān)參數(shù)等先驗(yàn)知識(shí)快速定位分割軸承。針對(duì)單閾值和多閾值算法在軸承檢測(cè)上的不足，提出了一種基于多次OSTU算法的軸承檢測(cè)方法，它很好地解決了前述兩種算法的弊端，采用八連通域法對(duì)處理后的軸承圖像進(jìn)行缺陷提取。研究利用不變矩和relief算法提取并篩選特征，減少在實(shí)際應(yīng)用中紋理特征提取的數(shù)量，使運(yùn)算速度加快，并利用BP人工神經(jīng)網(wǎng)絡(luò)對(duì)提取出來(lái)的紋理特征信息進(jìn)行聚類分析，結(jié)果證明了該方法的有效性。最后給出了軸承檢測(cè)系統(tǒng)的硬件結(jié)構(gòu)組成，并實(shí)現(xiàn)了檢測(cè)程序的設(shè)計(jì)和編制。
[Abstract]:Bearing is a very important and widely used rotating component in mechanical industry. In the production and use of bearings, in order to ensure the normal production and use of bearing products, semi-finished or finished bearings should be tested. At present, most bearing manufacturers use contact inspection method, that is, manual inspection method. The detection speed of this method is not only slow, but also the subjective factors of the examiner will affect the inspection result and the quality of the workpiece, especially the surface quality. The detection of bearing parts in the working environment is equally unfavorable. In view of the above problems, this paper aims to study a non-contact bearing quality detection method, that is, bearing quality detection based on image theory. This method not only avoids the disadvantages of contact inspection, but also can detect the bearing quality automatically. Non-manual intervention, with high-speed, high-precision, automatic and other characteristics, in line with the needs of large-scale production in today's society. At present, some existing defect detection methods based on product image are summarized. There are two kinds of methods: the first is based on the grayscale information of image product to judge the quality of product, this kind of method simply uses single threshold method to segment the product and defect information. However, some defect information may be lost; The second kind is based on the texture information of image products to judge the quality of products. This method has some shortcomings in detecting speed and clustering defect information. According to the characteristics of bearing image, this paper uses the prior knowledge such as least square method and relative parameters of bearing to locate and segment the bearing quickly. Aiming at the shortcomings of single threshold and multi-threshold algorithms in bearing detection, a bearing detection method based on multiple OSTU algorithm is proposed, which solves the disadvantages of the two algorithms well. The eight connected region method is used to extract the defect of the processed bearing image. In order to reduce the number of texture feature extraction in practical application and speed up the operation, the invariant moment and relief algorithm are used to extract and filter features, and BP artificial neural network is used to cluster the extracted texture feature information. The results show that the method is effective. Finally, the hardware structure of the bearing detection system is given, and the test program is designed and compiled.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TH133.3;TP274

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 楊明;宋麗華;;改進(jìn)的快速中值濾波算法在圖像去噪中的應(yīng)用[J];測(cè)繪工程;2011年03期

2 陳躍飛;王恒迪;鄧四二;;機(jī)器視覺(jué)檢測(cè)技術(shù)中軸承的定位算法[J];軸承;2010年04期

3 張龍;余玲玲;劉京南;;一種改進(jìn)的最大熵閾值分割方法[J];電子工程師;2006年11期

4 陳廉清;袁紅彬;王龍山;;SUSAN算子在微小軸承表面缺陷圖像分割中的應(yīng)用[J];光學(xué)技術(shù);2007年02期

5 張新明;張玉珊;李振云;;一種改進(jìn)的矩不變圖像分割方法[J];廣西師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年02期

6 苑津莎;張冬雪;李中;;基于改進(jìn)閾值法的小波去噪算法研究[J];華北電力大學(xué)學(xué)報(bào)(自然科學(xué)版);2010年05期

7 李偉;吳永祥;何濤;吳慶華;;基于坐標(biāo)變換的軸承缺陷檢測(cè)[J];湖北工業(yè)大學(xué)學(xué)報(bào);2008年01期

8 潘春雨;盧志剛;秦嘉;;基于區(qū)域閾值的圖像分割方法研究[J];火力與指揮控制;2011年01期

9 王菁菁,范影樂(lè);基于Hough變換的圓檢測(cè)技術(shù)[J];杭州電子科技大學(xué)學(xué)報(bào);2005年04期

10 焦圣喜;張利輝;江絳;;圖像檢測(cè)技術(shù)在工件在線分選中的應(yīng)用[J];機(jī)床與液壓;2010年05期

本文編號(hào)：2345940