【摘要】：變形監(jiān)測(cè)涉及工程地質(zhì)、結(jié)構(gòu)力學(xué)、計(jì)算機(jī)科學(xué)等諸多學(xué)科的知識(shí),它是一項(xiàng)跨學(xué)科的研究,并已發(fā)展為一門(mén)多學(xué)科交叉的邊緣學(xué)科。主要包括兩個(gè)方面的內(nèi)容：首先是把握工程建筑物的穩(wěn)定性,為安全運(yùn)行診斷提供必要的信息,以便及時(shí)發(fā)現(xiàn)問(wèn)題并采取措施；其次是科學(xué)上的意義,包括變形的機(jī)理,進(jìn)行反饋設(shè)計(jì)以及建立有效的變形預(yù)告模型。 在變形監(jiān)測(cè)方法和技術(shù)進(jìn)步的同時(shí),獲得的監(jiān)測(cè)數(shù)據(jù)也越來(lái)越豐富,這些監(jiān)測(cè)到的數(shù)據(jù)可以為變形體的狀態(tài)提供更多的信息,但是另一方面,使得變形分析變得更加復(fù)雜。為了提高變形分析及預(yù)報(bào)的準(zhǔn)確度,一方面需要對(duì)這些數(shù)據(jù)進(jìn)行誤差處理,提高觀測(cè)值的精度；另一方面需要利用信息融合技術(shù)對(duì)監(jiān)測(cè)數(shù)據(jù)進(jìn)行分析與處理,以便更準(zhǔn)確的進(jìn)行工程結(jié)構(gòu)健康的診斷以及災(zāi)害的預(yù)測(cè)預(yù)報(bào),由此,信息的分解與融合就成了變形分析的一個(gè)主要任務(wù)。 獨(dú)立分量分析是從多元統(tǒng)計(jì)數(shù)據(jù)中尋找其內(nèi)在因子或成分的一種方法。它是基于盲信號(hào)的分離而發(fā)展起來(lái)的,其突出的優(yōu)勢(shì)在于對(duì)于原始的信號(hào)不需要有太多的先驗(yàn)知識(shí),且能更靈活更有效地的表征信號(hào)的本質(zhì)結(jié)構(gòu),把獨(dú)立分量分析用于變形監(jiān)測(cè)信號(hào)處理中,在統(tǒng)計(jì)意義上更能反映工程建筑物變形的本質(zhì)特性。 本文把獨(dú)立分量分析方法引入到變形監(jiān)測(cè)數(shù)據(jù)處理與分析當(dāng)中,通過(guò)仿真實(shí)驗(yàn)與大壩監(jiān)測(cè)實(shí)測(cè)數(shù)據(jù)對(duì)獨(dú)立分量分析方法在信號(hào)分離及信號(hào)降噪處理方面進(jìn)行了研究,并在此基礎(chǔ)上,進(jìn)行多元回歸分析,建立變形預(yù)測(cè)回歸模型。 本文的主要研究工作分兩個(gè)部分：(一)基于獨(dú)立分量分析的信號(hào)處理 1.基于獨(dú)立分量分析的理論,在統(tǒng)計(jì)獨(dú)立原則的基礎(chǔ)上,通過(guò)分析多維觀測(cè)數(shù)據(jù)間的高階統(tǒng)計(jì)相關(guān)性,找出相互獨(dú)立的隱含信息成分,進(jìn)而得到獨(dú)立分量。通過(guò)仿真實(shí)驗(yàn)對(duì)信號(hào)進(jìn)行處理,實(shí)驗(yàn)結(jié)果表明,得到的獨(dú)立分量與源信號(hào)非常相似,只是次序和幅值不確定。 2.通過(guò)獨(dú)立分量分析對(duì)大壩監(jiān)測(cè)實(shí)測(cè)數(shù)據(jù)的處理,來(lái)說(shuō)明此方法在實(shí)際中的應(yīng)用效果。大壩變形監(jiān)測(cè)數(shù)據(jù)信號(hào)受到水位、溫度、時(shí)效以及噪聲的影響,通過(guò)設(shè)定一定數(shù)量的信號(hào)接收器,以滿足源信號(hào)數(shù)量小于等于接收器數(shù)量的要求,從而實(shí)現(xiàn)獨(dú)立分量分析在信號(hào)去噪中的應(yīng)用。 3.獨(dú)立分量分析方法并不能區(qū)分有用信號(hào)和噪聲,所以經(jīng)過(guò)此方法分離出的信號(hào),要根據(jù)時(shí)域、頻域及其他相關(guān)先驗(yàn)知識(shí)來(lái)加以區(qū)分。本文實(shí)例中分離出的信號(hào),通過(guò)先驗(yàn)知識(shí)以及與分量的特性對(duì)比,再把信號(hào)從時(shí)域轉(zhuǎn)到頻域進(jìn)行,可以把噪聲與有用信號(hào)進(jìn)行有效區(qū)分。 4.把獨(dú)立分量分析去噪與小波去噪的效果進(jìn)行比較。引入信噪比、均方差以及相關(guān)系數(shù)作為評(píng)價(jià)指標(biāo),通過(guò)仿真實(shí)驗(yàn)和實(shí)測(cè)數(shù)據(jù)的處理與分析,獨(dú)立分量分析去噪的效果優(yōu)于小波去噪,提取的獨(dú)立分量精度更高,去噪魯棒性更強(qiáng)。 (二)基于獨(dú)立分量分析的多元線性回歸分析 1.主分量回歸已經(jīng)在很多領(lǐng)域得到了應(yīng)用,本文中,在模擬數(shù)據(jù)的基礎(chǔ)上,利用最小二乘方法、主分量回歸以及獨(dú)立分量回歸對(duì)假定的回歸模型進(jìn)行求解,通過(guò)結(jié)果知道,獨(dú)立分量分析也可以應(yīng)用在多元線性回歸方法中,而且由此方法獲得的獨(dú)立分量可以對(duì)因變量進(jìn)行更好的解釋。 2.利用大壩的實(shí)測(cè)數(shù)據(jù),運(yùn)用獨(dú)立分量分析方法對(duì)監(jiān)測(cè)信號(hào)進(jìn)行處理,在噪聲信號(hào)被識(shí)別并剔除以后,利用剩下的因子去進(jìn)行多元線性回歸計(jì)算并得到回歸模型,在對(duì)實(shí)測(cè)值和預(yù)測(cè)值進(jìn)行比較的基礎(chǔ)上對(duì)此模型的預(yù)測(cè)精度進(jìn)行評(píng)價(jià)。
[Abstract]:Deformation monitoring involves the knowledge of many subjects such as engineering geology, structural mechanics, computer science and so on. It is an interdisciplinary study and has developed into a multi-disciplinary, cross-cutting edge discipline. The method mainly comprises two aspects: firstly, the stability of the engineering building is grasped, the necessary information is provided for the safe operation diagnosis so as to find the problems in time and take measures; secondly, the scientific significance, including the mechanism of deformation, and a feedback design and an effective deformation forecasting model are established. At the same time as the deformation monitoring method and the technical progress, the obtained monitoring data is more and more abundant, and the monitored data can provide more information for the state of the deformable body, but on the other hand, the deformation analysis becomes more complex. In order to improve the accuracy of deformation analysis and prediction, on the one hand, it is necessary to process the data and improve the precision of the observation value; on the other hand, it is necessary to use the information fusion technology to analyze and place the monitoring data In order to make more accurate diagnosis of structural health and forecast of disaster, the decomposition and fusion of information become one of the main functions of deformation analysis. The independent component analysis is to find out a factor or component in the multivariate statistical data. The method is developed based on the separation of blind signals, and has the advantages that too many prior knowledge is not required for the original signal, and the intrinsic structure of the signal can be more flexibly and effectively characterized, and the independent component analysis is used for deformation monitoring signals. In the treatment, the deformation of the engineering building can be more reflected in the statistical significance. In this paper, the independent component analysis method is introduced into the deformation monitoring data processing and analysis, and the independent component analysis method is studied by the simulation experiment and the dam monitoring data. Meta-regression analysis to set up a deformation pre-set Regression model is measured. The main research work in this paper is divided into two parts: (1) based on the independent score The signal processing of the quantity analysis is 1. Based on the theory of independent component analysis, on the basis of the statistical independent principle, the multi-dimensional observation data is analyzed. the high-order statistical correlation among the independent implicit information components is found out. The experiment results show that the obtained independent component is very similar to the source signal, and only the order and the amplitude value are not determined. 2. The process of monitoring the measured data of the dam by the independent component analysis will be described. The monitoring data signal of the dam deformation is affected by water level, temperature, aging and noise, and a certain number of signal receivers are set so as to meet the requirement of the number of the source signals to be less than or equal to the number of the receivers, so as to realize the independent component. 3. The independent component analysis method can not distinguish the useful signal and noise, so the signal separated by this method is based on time domain, frequency domain and frequency domain. The signal separated from the example is compared with the characteristic of the component, then the signal is transferred from the time domain to the frequency domain, and the signal can be transferred from the time domain to the frequency domain. The noise is effectively distinguished from the useful signal. 4. Separate the stand-alone components The effect of de-noising and small-wave de-noising is compared. The signal-to-noise ratio, the mean square deviation and the correlation coefficient are introduced as the evaluation index. the vertical component has higher precision and stronger denoising robustness. (2) A multi-element linear regression analysis based on independent component analysis. The main component regression has been applied in many fields. In this paper, on the basis of the simulation data, the main component is returned with the least two-by-by method. and the independent component analysis can be used in the multi-component linear regression method, and the method is obtained by the method, and the independent component analysis method is used for processing the monitoring signals, after the noise signals are identified and removed, the rest and the regression model is obtained, and the measured value and the predicted value are obtained.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.7;O212.1

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