音波法輸氣管道泄漏檢測系統(tǒng)實施與應用研究
發(fā)布時間：2019-03-16 21:36
【摘要】：近年來,國內油氣管道行業(yè)發(fā)展勢頭迅猛,儲運管道總長已達十多萬公里,但隨之也帶來了更加頻繁的管道安全事故。為了減輕和預防此類事故對人民生命財產(chǎn)安全造成的傷害,必須制定一套合理的油氣管線泄漏檢測與定位系統(tǒng)。由于目前已應用的管道泄漏檢測系統(tǒng)多采用單工況(泄漏工況、正常運行工況)辨識算法,因此對運行平穩(wěn)的管道應用效果較好,而對運營狀態(tài)變化較大的管道適應性卻較差。針對該技術難點本文以管道音波信號波動為檢測輸入量,進行了管道泄漏檢測與定位系統(tǒng)的開發(fā)。首先,為了解決管道運行工況辨識難的問題,本文引入善于模態(tài)識別的人工神經(jīng)網(wǎng)絡作為管道多工況判定識別算法。為了達到最好的多工況識別效果,本文還以工況區(qū)分度高、實時計算簡易和神經(jīng)網(wǎng)絡分辨性強為準則,對種類繁多的音波信號特征量(時域特征量、頻域特征量和時頻域聯(lián)合分析特征量)進行了優(yōu)選,同時結合優(yōu)選結果對多種類型的網(wǎng)絡應用效果進行了對比和分析,從而得出BP神經(jīng)網(wǎng)絡在泄漏判定準確率和抗干擾能力上相比其它幾種神經(jīng)網(wǎng)絡具有更高適用性。其次,BP網(wǎng)絡雖然使用廣泛,具有諸多優(yōu)越性能,但也存在一些如:訓練收斂不穩(wěn)定、易陷局部最優(yōu)、樣本依賴性強等缺陷。因此本文針對BP網(wǎng)絡的泛化能力、訓練收斂穩(wěn)定性以及模態(tài)識別精度三個方面提出了優(yōu)化算法。其中分別采用了貝葉斯歸一化訓練方法用來提高網(wǎng)絡泛化性能;改進的自適應遺傳算法用來提高網(wǎng)絡收斂穩(wěn)定性;模糊神經(jīng)網(wǎng)絡算法用來提高模態(tài)識別精度。通過多次試驗驗證可以得出,優(yōu)化后的BP網(wǎng)絡具有更好的網(wǎng)絡性能以及工況辨識效果。最后,為了編制出計算高效且適用性更強的長輸管線音波泄漏檢測與定位系統(tǒng),本文選擇編程簡便、外部接口較多且計算機系統(tǒng)適用性較強的Visual Basic6.0來編寫系統(tǒng)主體,同時引入MATLAB,利用其工具箱函數(shù)高效編寫工況判斷核心算法,并將Access數(shù)據(jù)庫嵌入VB從而實現(xiàn)了泄漏報警記錄的實時保存和顯示。總體來說,本文介紹了一種以管道音波信號為基礎,優(yōu)化后的泄漏多工況辨識BP神經(jīng)網(wǎng)絡為核心算法,采用NI-DAQmx、MATLAB、Visual Basic以及Access數(shù)據(jù)庫混合編制出的管道泄漏檢測與定位系統(tǒng),并通過系統(tǒng)有效性實驗驗證了該軟件系統(tǒng)在實驗室內的良好應用效果。
[Abstract]:In recent years, the domestic oil and gas pipeline industry has a rapid development momentum, the total length of storage and transportation pipelines has reached more than 100, 000 kilometers, but also brought more frequent pipeline safety accidents. In order to reduce and prevent the damage caused by such accidents to the safety of people's life and property, a reasonable leak detection and location system for oil and gas pipelines must be established. Because most of the pipeline leak detection systems used at present use the identification algorithm of single working condition (leakage condition, normal operation condition), so the application effect of pipeline with stable operation is better, but the adaptability of pipeline with great change of operation state is poor. In view of the technical difficulties, the pipeline leak detection and location system is developed in this paper, in which the acoustic wave signal fluctuation is used as the input to detect the pipeline leakage. Firstly, in order to solve the problem of difficult identification of pipeline operating conditions, this paper introduces the artificial neural network (Ann), which is good at modal identification, as the identification algorithm of pipeline multi-working conditions. In order to achieve the best identification effect of multi-working conditions, this paper is based on the criteria of high differentiation of working conditions, simple real-time calculation and strong resolution of neural network, for a wide variety of acoustic signal characteristics (time-domain characteristics, etc. The frequency domain characteristic quantity and the time-frequency domain joint analysis feature quantity are optimized. At the same time, many kinds of network application effects are compared and analyzed by combining the optimization results. It is concluded that BP neural network has higher applicability than other neural networks in the accuracy of leak detection and anti-jamming ability. Secondly, although BP network is widely used and has many superior performance, it also has some defects such as unstable training convergence, easy to fall into local optimization, strong sample dependence and so on. Therefore, this paper proposes an optimization algorithm for the generalization ability of BP network, the stability of training convergence and the accuracy of modal identification. The Bayesian normalization training method is used to improve the generalization performance of the network, the improved adaptive genetic algorithm is used to improve the convergence stability of the network, and the fuzzy neural network algorithm is used to improve the accuracy of modal identification. Through many experiments, it can be concluded that the optimized BP network has better network performance and better working condition identification effect. Finally, in order to develop a sound leakage detection and location system for long-distance pipeline with high efficiency and applicability, this paper chooses Visual Basic6.0, which has simple programming, more external interfaces and stronger applicability of computer system, to program the main body of the system. At the same time, MATLAB, is introduced to use its toolbox function to write the core algorithm of working condition judgment efficiently, and the Access database is embedded into VB to realize the real-time storage and display of leak alarm records. In general, this paper introduces an optimized leakage multi-condition identification BP neural network algorithm based on the pipeline acoustic signal, and uses NI-DAQmx,MATLAB, as the core algorithm. A pipeline leak detection and location system based on Visual Basic and Access database is developed. The effectiveness of the system is verified by the experimental results in the laboratory.
【學位授予單位】：中國石油大學(華東)
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP183;TE973.6
[Abstract]:In recent years, the domestic oil and gas pipeline industry has a rapid development momentum, the total length of storage and transportation pipelines has reached more than 100, 000 kilometers, but also brought more frequent pipeline safety accidents. In order to reduce and prevent the damage caused by such accidents to the safety of people's life and property, a reasonable leak detection and location system for oil and gas pipelines must be established. Because most of the pipeline leak detection systems used at present use the identification algorithm of single working condition (leakage condition, normal operation condition), so the application effect of pipeline with stable operation is better, but the adaptability of pipeline with great change of operation state is poor. In view of the technical difficulties, the pipeline leak detection and location system is developed in this paper, in which the acoustic wave signal fluctuation is used as the input to detect the pipeline leakage. Firstly, in order to solve the problem of difficult identification of pipeline operating conditions, this paper introduces the artificial neural network (Ann), which is good at modal identification, as the identification algorithm of pipeline multi-working conditions. In order to achieve the best identification effect of multi-working conditions, this paper is based on the criteria of high differentiation of working conditions, simple real-time calculation and strong resolution of neural network, for a wide variety of acoustic signal characteristics (time-domain characteristics, etc. The frequency domain characteristic quantity and the time-frequency domain joint analysis feature quantity are optimized. At the same time, many kinds of network application effects are compared and analyzed by combining the optimization results. It is concluded that BP neural network has higher applicability than other neural networks in the accuracy of leak detection and anti-jamming ability. Secondly, although BP network is widely used and has many superior performance, it also has some defects such as unstable training convergence, easy to fall into local optimization, strong sample dependence and so on. Therefore, this paper proposes an optimization algorithm for the generalization ability of BP network, the stability of training convergence and the accuracy of modal identification. The Bayesian normalization training method is used to improve the generalization performance of the network, the improved adaptive genetic algorithm is used to improve the convergence stability of the network, and the fuzzy neural network algorithm is used to improve the accuracy of modal identification. Through many experiments, it can be concluded that the optimized BP network has better network performance and better working condition identification effect. Finally, in order to develop a sound leakage detection and location system for long-distance pipeline with high efficiency and applicability, this paper chooses Visual Basic6.0, which has simple programming, more external interfaces and stronger applicability of computer system, to program the main body of the system. At the same time, MATLAB, is introduced to use its toolbox function to write the core algorithm of working condition judgment efficiently, and the Access database is embedded into VB to realize the real-time storage and display of leak alarm records. In general, this paper introduces an optimized leakage multi-condition identification BP neural network algorithm based on the pipeline acoustic signal, and uses NI-DAQmx,MATLAB, as the core algorithm. A pipeline leak detection and location system based on Visual Basic and Access database is developed. The effectiveness of the system is verified by the experimental results in the laboratory.
【學位授予單位】：中國石油大學(華東)
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TP183;TE973.6
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