本文選題：油液分析 + 狀態(tài)監(jiān)測　； 參考：《華南理工大學》2011年碩士論文

【摘要】：隨著國民經(jīng)濟的發(fā)展和科學技術(shù)水平的不斷進步,機械設(shè)備朝著大型化、復雜化、連續(xù)長周期運行方向發(fā)展,因此開展機械設(shè)備狀態(tài)監(jiān)測和故障診斷顯得越來越重要。潤滑油是機械設(shè)備的血液,它的狀態(tài)與設(shè)備磨損狀態(tài)最為密切。通過對潤滑油的檢測分析,可以很好的對設(shè)備的運行狀態(tài)進行監(jiān)測。 本文以油液分析為基礎(chǔ),對某石化公司的大型超高壓鍋爐給水泵設(shè)備進行長期監(jiān)測,并開發(fā)了一套磨粒識別系統(tǒng)軟件,該軟件可以對幾種常見磨粒進行識別,輔助油液監(jiān)測的展開。 在對設(shè)備進行監(jiān)測的過程中,采用了鐵譜分析與油液理化指標分析的綜合分析方法。一方面利用趨勢圖三線值法、鐵譜磨粒的定性和定量分析描述了設(shè)備的磨損狀態(tài);另一方面又通過監(jiān)測油液的粘度、水分、酸值、破乳化值、機械雜質(zhì)、閃點6項理化指標,分析了油品的質(zhì)量與衰變程度,確保設(shè)備良好的潤滑狀態(tài)。在為期一年的監(jiān)測中,通過油液分析及時發(fā)現(xiàn)問題并解決問題,保證了設(shè)備的潤滑狀態(tài)和正常磨損狀態(tài),使設(shè)備能夠良好的運行。 在現(xiàn)代油液分析中,主要通過磨粒來判斷設(shè)備故障程度和失效類型。由于傳統(tǒng)的磨粒分析與識別依賴于分析人員的知識和經(jīng)驗,大大限制了油液分析技術(shù)的發(fā)展。本文以Visual C++ 6.0為軟件開發(fā)平臺,開發(fā)了一套磨粒識別系統(tǒng)。這套系統(tǒng)結(jié)合了油液分析技術(shù)、圖像處理技術(shù)和智能識別技術(shù),具有界面友好,操作簡單,運行效率高,兼容性和擴展能力強等特點。要完成對磨粒的識別,必須解決鐵譜磨粒圖像中磨粒與背景以及磨粒與磨粒之間的分割問題。通過圖像縮放、灰度化、二值化、形態(tài)學運算、輪廓提取、鏈碼提取、種子填充等圖像處理算法的結(jié)合,很好的解決了磨粒圖像的分割問題。最后,模仿人對物體的認識,提取了磨粒的形狀、紋理以及顏色特征,結(jié)合BP神經(jīng)網(wǎng)絡(luò)分類算法,成功地對5種典型磨粒(正常磨粒、切削磨粒、球狀磨粒、嚴重滑動磨粒、疲勞磨損磨粒)進行分類識別。
[Abstract]:With the development of national economy and the progress of science and technology, mechanical equipment is developing towards the direction of large scale, complication and continuous long period operation. Therefore, it is more and more important to carry out condition monitoring and fault diagnosis of machinery and equipment. Lubricating oil is the blood of mechanical equipment, its condition and equipment wear state most closely. Through the detection and analysis of the lubricating oil, the running state of the equipment can be monitored well. On the basis of oil analysis, this paper has carried out long-term monitoring of feedwater pump equipment of a large ultra-high pressure boiler in a petrochemical company, and developed a set of abrasive particle recognition system software, which can identify several kinds of common abrasive particles. Development of auxiliary oil monitoring. In the process of monitoring the equipment, a comprehensive analysis method of ferrography analysis and oil physicochemical index analysis was adopted. On the one hand, the wear state of the equipment is described qualitatively and quantitatively by using the three line value method of trend diagram, on the other hand, the physical and chemical indexes of oil viscosity, moisture, acid value, breaking emulsification value, mechanical impurity and flash point are monitored. The quality and decay degree of the oil are analyzed to ensure the good lubrication condition of the equipment. In the course of monitoring for one year, the problems can be found and solved in time through oil analysis, which ensures the lubrication state and normal wear state of the equipment and makes the equipment run well. In modern oil analysis, the fault degree and failure type of equipment are judged by abrasive particles. The traditional analysis and identification of abrasive particles depend on the knowledge and experience of the analysts, which greatly limits the development of oil analysis technology. In this paper, a wear particle recognition system is developed on the platform of Visual C 6.0. The system combines oil analysis technology, image processing technology and intelligent recognition technology. It has the advantages of friendly interface, simple operation, high operation efficiency, strong compatibility and expansibility. In order to recognize the wear particles, we must solve the problem of the segmentation between the wear particles and the background and between the wear particles and the wear particles in the image of ferrographic wear particles. Through the combination of image scaling, grayscale, binarization, morphological operation, contour extraction, chain code extraction, seed filling and other image processing algorithms, the problem of abrasive image segmentation is well solved. Finally, the shape, texture and color features of abrasive particles are extracted by imitating people's understanding of objects. Combined with BP neural network classification algorithm, five typical wear particles (normal wear particles, cutting wear particles, ball wear particles, serious sliding wear particles) are successfully processed. Fatigue wear abrasives) are classified and identified.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2011
【分類號】：TH165.3

【參考文獻】

相關(guān)期刊論文 前10條

1 李樹剛;周睿霞;;潤滑油開口閃點不確定度的評定[J];化學工程與裝備;2010年09期

2 樊中秀;;汽輪機油添加防銹劑對酸值影響的考察和監(jiān)督[J];華北電力技術(shù);1987年Z1期

3 左洪福,吳振鋒,楊忠;雙BP神經(jīng)網(wǎng)絡(luò)在磨損顆粒自動識別中的應(yīng)用[J];航空學報;2000年04期

4 李艷軍,左洪福,陳果;基于灰色聚類的磨粒自動識別[J];航空學報;2003年04期

5 黎瓊煒;新型油液在線監(jiān)控技術(shù)[J];測控技術(shù);2005年04期

6 陳閩杰;李秋秋;;油壓曲線監(jiān)測在滑動軸承磨損診斷中的應(yīng)用[J];機床與液壓;2010年21期

7 吳振鋒,左洪福,楊忠;磨損微粒顯微形態(tài)學特征量化描述體系[J];交通運輸工程學報;2001年01期

8 韓亞麗;TBM狀態(tài)監(jiān)測與故障診斷[J];建筑機械;2003年09期

9 劉金龍,葛世榮,陳國安,吳振芳;磨合磨損分形預(yù)測研究[J];摩擦學學報;2000年04期

10 樊建春,楊明忠,李杰;磨損微粒識別的模糊決策樹方法[J];潤滑與密封;1996年06期

相關(guān)碩士學位論文 前9條

1 苑茂存;基于數(shù)字圖像處理技術(shù)的磨損磨粒識別方法研究[D];燕山大學;2004年

2 袁麗娟;基于多種分析技術(shù)的油液監(jiān)測診斷專家系統(tǒng)開發(fā)[D];鄭州大學;2006年

3 張工;基于VC++的DPIV圖像后處理軟件系統(tǒng)[D];大連理工大學;2006年

4 王輝;基于灰度共生矩陣木材表面紋理模式識別方法的研究[D];東北林業(yè)大學;2007年

5 張勇;基于圖像處理的發(fā)動機潤滑油磨粒檢測技術(shù)研究[D];西北農(nóng)林科技大學;2008年

6 沈如蕓;鐵譜磨粒圖像預(yù)處理與磨粒識別技術(shù)研究[D];昆明理工大學;2008年

7 譚林秋;基于VC++的數(shù)字圖像處理系統(tǒng)的開發(fā)及算法研究[D];西安理工大學;2008年

8 劉凱;油液在線監(jiān)測電容傳感器的研制及在線測試方法研究[D];沈陽理工大學;2008年

9 卞利;基于靜電和圖像分析的油液在線監(jiān)測系統(tǒng)研究[D];南京航空航天大學;2009年

，

本文編號：2053884