本文關(guān)鍵詞： 礦漿管道泄漏檢測(cè) 壓力信號(hào)去噪 敏感奇異值分解 小波近似熵 極限學(xué)習(xí)機(jī)　出處：《昆明理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著管道運(yùn)輸?shù)膹V泛應(yīng)用,管道的安全運(yùn)行也得到了人們的關(guān)注。管道發(fā)生泄漏時(shí)會(huì)造成非常嚴(yán)重的后果,包括經(jīng)濟(jì)和生產(chǎn)的損失、自然資源的浪費(fèi)和環(huán)境因素的破壞等,尤其是后兩者是無法補(bǔ)償?shù)?迫切地需要能夠?qū)艿佬孤┻M(jìn)行準(zhǔn)確及時(shí)檢測(cè)的方法。管道泄漏檢測(cè)作為管道安全運(yùn)行重要構(gòu)成部分成為管道運(yùn)輸中的重要研究?jī)?nèi)容。管道泄漏檢測(cè)屬于實(shí)際應(yīng)用問題,管道若發(fā)生泄漏事故,此段管道要報(bào)廢進(jìn)行更換,因此管道泄漏檢測(cè)需借助管道泄漏實(shí)驗(yàn)系統(tǒng)進(jìn)行研究工作,漿體管道泄漏實(shí)驗(yàn)系統(tǒng)為管道泄漏檢測(cè)提供實(shí)驗(yàn)數(shù)據(jù)基礎(chǔ)。為了給下一步管道泄漏檢測(cè)提供良好基礎(chǔ),需要對(duì)壓力信號(hào)進(jìn)行預(yù)處理,抑制管道壓力信號(hào)中噪聲干擾。在管道的實(shí)際工業(yè)運(yùn)行中,要根據(jù)實(shí)際生產(chǎn)需求對(duì)管道礦漿的輸送量進(jìn)行工況調(diào)整,此時(shí)管道的壓力波變換與管道發(fā)生泄漏時(shí)產(chǎn)生的負(fù)壓波相似,如何排除工況調(diào)整干擾準(zhǔn)確的對(duì)管道進(jìn)行泄漏檢測(cè)避免錯(cuò)報(bào)、漏報(bào)具有重要的意義。論文的主要研究工作如下:(1)針對(duì)礦漿管道特殊運(yùn)輸方式和復(fù)雜輸送機(jī)理的問題,通過理論研究與實(shí)際經(jīng)驗(yàn)設(shè)計(jì)并搭建了礦漿管道泄漏實(shí)驗(yàn)系統(tǒng)。理論計(jì)算出合理的管材類型和管道壁厚從而克服了管材磨蝕問題,設(shè)計(jì)了動(dòng)力系統(tǒng)、混漿及清洗系統(tǒng)和測(cè)量采集系統(tǒng)。通過實(shí)驗(yàn)證明了管道泄漏系統(tǒng)的可行性,并為下一步的研究工作提供有效的實(shí)驗(yàn)數(shù)據(jù)。(2)針對(duì)管道壓力泄漏信號(hào)去噪的問題,采用基于敏感因子奇異值分解的管道泄漏壓力信號(hào)去噪的方法。該方法首先對(duì)原始信號(hào)構(gòu)造Hankel矩陣再進(jìn)行SVD分解,將分解后得到的分量信號(hào)利用敏感因子找出敏感分量,最后通過定位因子選擇敏感分量所對(duì)應(yīng)的奇異值進(jìn)行信號(hào)重構(gòu),并用該方法對(duì)礦漿管道泄漏實(shí)驗(yàn)系統(tǒng)中采集到的壓力信號(hào)進(jìn)行降噪處理,作為信號(hào)的預(yù)處理為管道泄漏檢測(cè)提供良好的基礎(chǔ)。(3)針對(duì)從管道泄漏檢測(cè)中的非線性和非平穩(wěn)壓力信號(hào)中提取泄漏特征難的問題,采用一種小波分解近似熵和極限學(xué)習(xí)機(jī)相結(jié)合的管道泄漏檢測(cè)方法。首先對(duì)管道壓力信號(hào)進(jìn)行小波分解,選取含有主要特征的前3層分量,將前3層分量的近似熵和峭度值作為特征向量,最后通過極限學(xué)習(xí)機(jī)對(duì)特征向量進(jìn)行識(shí)別分類�；谛〔ㄗ儞Q近似熵和極限學(xué)習(xí)機(jī)相結(jié)合的方法能有效準(zhǔn)確的進(jìn)行管道泄漏能識(shí)別。通過理論計(jì)算與實(shí)際經(jīng)驗(yàn)相結(jié)合設(shè)計(jì)了漿體管道泄漏實(shí)驗(yàn)驗(yàn)系統(tǒng),并為管道泄漏檢測(cè)提供實(shí)驗(yàn)數(shù)據(jù);將敏感因子引入傳統(tǒng)的奇異值分解,通過信號(hào)重構(gòu)有效的抑制管道噪聲干擾,為管道泄漏檢測(cè)提供良好基礎(chǔ);采用近似熵與小波變換結(jié)合的方法,提取工況調(diào)整狀態(tài)、泄漏狀態(tài)和正常運(yùn)行狀態(tài)時(shí)特征向量,通過極限學(xué)習(xí)機(jī)有效準(zhǔn)確進(jìn)行識(shí)別分類,為管道準(zhǔn)確的泄漏檢測(cè)提供新方法,具有一定理論與實(shí)際意義。
[Abstract]:With the wide application of pipeline transportation, people also pay attention to the safe operation of pipeline. The leakage of pipeline will cause very serious consequences, including the loss of economy and production, the waste of natural resources and the destruction of environmental factors, etc. In particular, the latter two are irreparable. It is urgent to be able to detect pipeline leakage accurately and timely. As an important part of pipeline safe operation, pipeline leakage detection is an important research content in pipeline transportation. Pipeline leakage detection is a practical application problem. In the event of pipeline leakage accident, the pipeline has to be scrapped and replaced, so the pipeline leakage detection needs to be studied with the pipeline leakage experimental system. The slurry pipeline leak experiment system provides the experimental data basis for pipeline leakage detection. In order to provide a good basis for pipeline leakage detection in the next step, it is necessary to preprocess the pressure signal. In the actual industrial operation of the pipeline, it is necessary to adjust the transportation rate of the pipeline slurry according to the actual production demand. At this time, the pressure wave transformation of pipeline is similar to the negative pressure wave generated by pipeline leakage. How to eliminate the interference of adjustment of working conditions and accurately detect the pipeline leakage to avoid misreporting, The main research work of this paper is as follows: 1) aiming at the problems of special transportation mode and complex conveyer mechanism of slurry pipeline, Based on the theoretical research and practical experience, the experimental system of slurry pipeline leakage is designed and built. The reasonable pipe type and pipe wall thickness are calculated theoretically, thus the problem of pipe abrasion is overcome, and the power system is designed. The feasibility of pipeline leakage system is proved by experiments, and effective experimental data is provided for further research. The method of pipeline leakage pressure signal denoising based on sensitivity factor singular value decomposition (SVD) is adopted. Firstly, the original signal is constructed by Hankel matrix and then decomposed by SVD, and the sensitive component is found by using the sensitivity factor. Finally, the singular value corresponding to the sensitive component is selected by the location factor to reconstruct the signal, and the pressure signal collected in the slurry pipeline leakage experiment system is de-noised by the method. As the preprocessing of the signal, it provides a good foundation for pipeline leakage detection. Aiming at the problem that it is difficult to extract leakage characteristics from nonlinear and non-stationary pressure signals in pipeline leakage detection, A method of pipeline leakage detection based on wavelet decomposition approximate entropy and ultimate learning machine is adopted. Firstly, the pressure signal of pipeline is decomposed by wavelet, and the first three layers with main characteristics are selected. Using the approximate entropy and kurtosis of the first three layers as eigenvector, Finally, the eigenvector is recognized and classified by the extreme learning machine. Based on the combination of wavelet transform approximate entropy and ultimate learning machine, the pipeline leakage energy can be identified effectively and accurately. Through theoretical calculation and practical experience, the pipeline leakage energy can be identified effectively and accurately. Combined with the design of slurry pipeline leakage test system, It also provides experimental data for pipeline leakage detection, introduces the sensitive factor into the traditional singular value decomposition, effectively suppresses the pipeline noise through signal reconstruction, and provides a good foundation for pipeline leakage detection. The method of combining approximate entropy with wavelet transform is used to extract the characteristic vectors of operating condition adjustment state, leakage state and normal running state, and to identify and classify effectively and accurately through the ultimate learning machine, which provides a new method for accurate leak detection of pipeline. It has certain theoretical and practical significance.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD50

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