油氣管道內(nèi)檢測數(shù)據(jù)比對分析方法及應(yīng)用
發(fā)布時間：2018-10-09 07:26
【摘要】：根據(jù)國家法規(guī)與企業(yè)相關(guān)規(guī)范要求,管道運營公司必須在管道的科研、設(shè)計、施工、運行各個階段,開展管道的完整性管理。作為完整性評價最為有效的技術(shù),管道內(nèi)檢測對于缺陷的識別具有重要意義。通過比較分析同一管道的多輪內(nèi)檢測數(shù)據(jù),可得到豐富的有價值的信息,包括缺陷增長情況、管道腐蝕速率等,進而分析缺陷形成原因,評估腐蝕發(fā)展狀況,確定下一輪內(nèi)檢測的時間,提高完整性管理水平。因此一套可靠的油氣管道內(nèi)檢測數(shù)據(jù)比對分析方法對于保障管道本質(zhì)安全、確保管道完整性、減少人身及財產(chǎn)損失具有重要的理論意義和實際意義。由于部分因素的不確定性和誤差及多輪內(nèi)檢測過程的差異性,使得一些檢測結(jié)果與開挖結(jié)果有所不符,存在誤報的可能。為此,提出了內(nèi)檢測起始終止位置的校對算法,基于數(shù)據(jù)挖掘的管道焊縫數(shù)據(jù)對齊算法,基于貝葉斯理論、遺傳算法理論及模糊聚類的內(nèi)檢測不對齊焊縫類型判斷模糊智能算法,內(nèi)檢測特征匹配算法等一系列算法,完成了報告特征的精確匹配。并結(jié)合內(nèi)檢測信號、開挖結(jié)果、其他檢測結(jié)果,對內(nèi)檢測特征進行比對�；谔卣鞅葘Y(jié)果開展了腐蝕增長率計算方法研究。采用直接方法計算了各類特征增長率。但鑒于直接方法同時估計增長尺寸與增長周期的相互干擾,引入聚類分析技術(shù)和貝葉斯模型框架以估計缺陷實際深度,推薦“基于聚類技術(shù)和貝葉斯模型框架的腐蝕增長率估計”的方法,使缺陷實際深度與增長周期的估計過程相對獨立,消除干擾。為了對內(nèi)檢測運營商及內(nèi)檢測器制造商形成反饋,評估了內(nèi)檢測結(jié)果,并對內(nèi)檢測器的探測率、誤報率、識別率和尺寸精度等指標進行評價,完成了管道缺陷數(shù)據(jù)綜合評價方法研究,以幫助管道運營商選擇內(nèi)檢測器,同時有利于內(nèi)檢測器的改進。同時,基于以上研究完成了“油氣管道內(nèi)檢測數(shù)據(jù)比對分析系統(tǒng)”的開發(fā)。系統(tǒng)包含項目管理、數(shù)據(jù)錄入、數(shù)據(jù)查看、焊縫對齊、特征匹配、特征比對、腐蝕增長率計算7個模塊,輔助完成內(nèi)檢測數(shù)據(jù)比對分析過程中海量數(shù)據(jù)的處理分析。
[Abstract]:According to the requirements of national regulations and relevant enterprise codes, pipeline operation companies must carry out pipeline integrity management in all stages of pipeline research, design, construction and operation. As the most effective technology for integrity evaluation, pipeline detection is very important for defect identification. By comparing and analyzing the data of multi-round inspection of the same pipeline, we can get rich and valuable information, including the growth of defects, the corrosion rate of pipelines, etc., and then analyze the causes of the defects and evaluate the development of corrosion. Determine the time for the next round of testing and improve integrity management. Therefore, a set of reliable methods for comparing and analyzing the data of oil and gas pipeline is of great theoretical and practical significance for ensuring pipeline safety, ensuring pipeline integrity and reducing personal and property losses. Because of the uncertainty and error of some factors and the difference of the detection process in many wheels, some of the test results do not conform to the excavation results, and there is the possibility of false alarm. Therefore, a proofreading algorithm is proposed to detect the starting and terminating position, which is based on data mining, pipeline weld data alignment and Bayesian theory. A series of algorithms, such as genetic algorithm theory, fuzzy intelligent algorithm to judge the type of unaligned weld seam in fuzzy clustering, and matching algorithm of inner detection feature, are used to complete the accurate matching of the report features. Combined with internal detection signal, excavation results, other detection results, internal detection characteristics are compared. The calculation method of corrosion growth rate is studied based on the results of characteristic comparison. All kinds of characteristic growth rates are calculated by direct method. However, in view of the direct method to estimate the mutual interference between growth size and growth cycle simultaneously, clustering analysis and Bayesian model framework are introduced to estimate the actual depth of defects. The method of "estimation of corrosion growth rate based on clustering technology and Bayesian model framework" is recommended, which makes the estimation process of actual depth and growth cycle of defects relatively independent and eliminates interference. In order to provide feedback to internal detection operators and internal detector manufacturers, the internal detection results are evaluated, and the detection rate, false alarm rate, recognition rate and dimensional accuracy of the internal detector are evaluated. The comprehensive evaluation method of pipeline defect data is completed in order to help pipeline operators select inner detector and improve the inner detector. At the same time, based on the above research, the development of "oil-gas pipeline detection data analysis system" is completed. The system includes seven modules: project management, data input, data viewing, weld alignment, feature matching, feature comparison and corrosion growth rate calculation.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TE973.6
，
本文編號：2258476
[Abstract]:According to the requirements of national regulations and relevant enterprise codes, pipeline operation companies must carry out pipeline integrity management in all stages of pipeline research, design, construction and operation. As the most effective technology for integrity evaluation, pipeline detection is very important for defect identification. By comparing and analyzing the data of multi-round inspection of the same pipeline, we can get rich and valuable information, including the growth of defects, the corrosion rate of pipelines, etc., and then analyze the causes of the defects and evaluate the development of corrosion. Determine the time for the next round of testing and improve integrity management. Therefore, a set of reliable methods for comparing and analyzing the data of oil and gas pipeline is of great theoretical and practical significance for ensuring pipeline safety, ensuring pipeline integrity and reducing personal and property losses. Because of the uncertainty and error of some factors and the difference of the detection process in many wheels, some of the test results do not conform to the excavation results, and there is the possibility of false alarm. Therefore, a proofreading algorithm is proposed to detect the starting and terminating position, which is based on data mining, pipeline weld data alignment and Bayesian theory. A series of algorithms, such as genetic algorithm theory, fuzzy intelligent algorithm to judge the type of unaligned weld seam in fuzzy clustering, and matching algorithm of inner detection feature, are used to complete the accurate matching of the report features. Combined with internal detection signal, excavation results, other detection results, internal detection characteristics are compared. The calculation method of corrosion growth rate is studied based on the results of characteristic comparison. All kinds of characteristic growth rates are calculated by direct method. However, in view of the direct method to estimate the mutual interference between growth size and growth cycle simultaneously, clustering analysis and Bayesian model framework are introduced to estimate the actual depth of defects. The method of "estimation of corrosion growth rate based on clustering technology and Bayesian model framework" is recommended, which makes the estimation process of actual depth and growth cycle of defects relatively independent and eliminates interference. In order to provide feedback to internal detection operators and internal detector manufacturers, the internal detection results are evaluated, and the detection rate, false alarm rate, recognition rate and dimensional accuracy of the internal detector are evaluated. The comprehensive evaluation method of pipeline defect data is completed in order to help pipeline operators select inner detector and improve the inner detector. At the same time, based on the above research, the development of "oil-gas pipeline detection data analysis system" is completed. The system includes seven modules: project management, data input, data viewing, weld alignment, feature matching, feature comparison and corrosion growth rate calculation.
【學(xué)位授予單位】：東北石油大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TE973.6
，
本文編號：2258476
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