【摘要】：多相流作為自然界普遍存在的一種現(xiàn)象,不僅是由于被測介質(zhì)的介電常數(shù)會隨著溫度等環(huán)境的變化而變化,而且還由于被測場域中存在其他介質(zhì),會使得測量時出現(xiàn)介質(zhì)未知的情況,并且其流動特性十分復(fù)雜,難以用數(shù)學(xué)模型完全描述,因而給測量帶來困難。多相流的在線檢測成像技術(shù)和實施方案多處在實驗室測試研究階段,只有少數(shù)已商品化,并能普遍應(yīng)用于在線檢測中,且多適用于兩相流,尚缺少一種適用于介質(zhì)未知且相數(shù)自適應(yīng)的圖像重建算法,所以需要進(jìn)一步的研究推動其發(fā)展和實用化。而學(xué)習(xí)是機器學(xué)習(xí)的一大特點,通過學(xué)習(xí)介質(zhì)的不同變化,如分布變化、介電常數(shù)變化等,可以及時調(diào)整算法參數(shù),這個特點適用于解決多相流在線檢測問題。ECT技術(shù)是一種用于多相流在線檢測技術(shù),廣泛存在于海上石油開采等工業(yè)化領(lǐng)域研究中。然而,ECT技術(shù)在應(yīng)用上存在不少問題和難點,尚且還不完善。本文從微弱電容處理和介電常數(shù)未知情況下的圖像重建算法研究的角度出發(fā),基于機器學(xué)習(xí)的方法研究ECT的圖像重建,主要工作和貢獻(xiàn)如下:(1)針對ECT的“軟場”特征,研究了數(shù)據(jù)預(yù)處理部分的ECT電容歸一化模型。分析了電容歸一化的物理特性并結(jié)合并聯(lián)歸一化方法,建立了一種加權(quán)值的電容歸一化模型,并應(yīng)用于基于SVM的圖像重建中。通過與并聯(lián)模型對比得出,在相數(shù)確定以及管內(nèi)介質(zhì)無變化的條件下,該方法不僅適用于兩相流還適用于兩相流以上的多相流,其圖像重建與真實模型的相關(guān)性要高于并聯(lián)歸一化方法。(2)在ECT系統(tǒng)的實際應(yīng)用中,兩相流和多相流是最普遍的流體情況。不僅是由于被測介質(zhì)的介電常數(shù)會隨著溫度等環(huán)境的變化而變化,而且還由于被測場域中存在其他介質(zhì),會使得測量時出現(xiàn)介質(zhì)未知的情況。本文利用機器學(xué)習(xí)中的支持向量機方法具有良好泛化性的特點,提出采用基于SVC的電容層析成像圖像重建算法對未知介電常數(shù)對象進(jìn)行圖像重建,仿真結(jié)果表明,在相數(shù)確定的情況,該算法能有效適應(yīng)介質(zhì)多樣性變化,即對于不同介質(zhì),該算法都能有較高的圖像重建精度。(3)現(xiàn)存的ECT重建算法往往只對相數(shù)確定以及介質(zhì)無變化的情況進(jìn)行重新構(gòu)建。針對該問題,作者建立了基于SVM決策樹的機器學(xué)習(xí)方法進(jìn)行自適應(yīng)相數(shù)預(yù)測模型,通過SVM決策樹的方法實現(xiàn)在相數(shù)不確定的情況下,對管內(nèi)介質(zhì)進(jìn)行預(yù)測,實驗結(jié)果表明,在相數(shù)不確定的情況下,該方法能較好的區(qū)分管內(nèi)相數(shù)以及管內(nèi)包含的介質(zhì)。最后,結(jié)合上述方法,設(shè)計了基于SVM決策樹的自適應(yīng)相數(shù)ECT圖像重建算法,初步分析了相數(shù)未知以及介質(zhì)變化時,如何進(jìn)行ECT圖像重建,并達(dá)到提高重建精度的目的,為ECT技術(shù)提供了一種新的研究思路。
[Abstract]:As a common phenomenon in nature, multiphase flow is not only due to the change of dielectric constant in the measured medium with the change of temperature and other environments, but also due to the existence of other media in the measured field. It will make the medium unknown in the measurement, and its flow characteristics are very complex, so it is difficult to describe it completely by the mathematical model, so it is difficult to measure. The on-line detection and imaging technology and implementation scheme of multiphase flow are mostly in the laboratory test and research stage, only a few of them have been commercialized, and can be widely used in on-line detection, and most of them are suitable for two-phase flow. There is still a lack of an image reconstruction algorithm which is suitable for unknown media and adaptive phase number, so further research is needed to promote its development and practicality. Learning is one of the characteristics of machine learning. Through the change of learning medium, such as distribution change and dielectric constant change, the algorithm parameters can be adjusted in time. ECT technology is a kind of on-line multi-phase flow detection technology, which is widely used in the field of offshore oil exploitation and other industrial research. However, there are many problems and difficulties in the application of ECT technology, which is not perfect yet. From the point of view of weak capacitance processing and image reconstruction algorithm with unknown dielectric constant, this paper studies the image reconstruction of ECT based on machine learning method. The main work and contribution are as follows: (1) aiming at the "soft field" feature of ECT, In this paper, the normalized model of ECT capacitance in the data preprocessing part is studied. Based on the analysis of the physical characteristics of capacitance normalization and the parallel normalization method, a weighted capacitance normalization model is established and applied to image reconstruction based on SVM. Compared with the parallel model, the method is applicable not only to the two-phase flow but also to the multi-phase flow above the two-phase flow under the condition that the number of phases is determined and the medium in the tube is not changed. The correlation between the image reconstruction and the real model is higher than the parallel normalization method. (2) in the practical application of ECT system, two-phase flow and multi-phase flow are the most common cases of fluid. It is not only because the dielectric constant of the measured medium changes with the change of temperature and other environments, but also because of the existence of other media in the measured field, which makes the measurement medium unknown. In this paper, the SVC-based electrical capacitance tomography image reconstruction algorithm is proposed to reconstruct the unknown dielectric constant object by using the support vector machine (SVM) method in machine learning. The simulation results show that the image reconstruction algorithm is based on the electrical capacitance tomography (ECT). In the case of phase number determination, the algorithm can effectively adapt to the change of media diversity, that is, for different media, This algorithm can have high image reconstruction accuracy. (3) the existing ECT reconstruction algorithms are usually reconstructed only when the number of phases is determined and the medium is unchanged. In order to solve this problem, the author established a machine learning method based on SVM decision tree to predict the number of phases. The method of SVM decision tree is used to predict the medium in the tube when the number of phases is uncertain. The experimental results show that: When the number of phases is uncertain, the method can distinguish the number of phases in the tube and the medium contained in the tube. Finally, based on the above methods, an adaptive phase number ECT image reconstruction algorithm based on SVM decision tree is designed. When the phase number is unknown and the medium changes, how to reconstruct the ECT image is preliminarily analyzed, and the purpose of improving the reconstruction accuracy is achieved. It provides a new research idea for ECT technology.
【學(xué)位授予單位】：上海海洋大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TP181

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