單通道ICA及其在變形分析中的應(yīng)用
發(fā)布時間：2019-01-04 12:16
【摘要】：從變形監(jiān)測數(shù)據(jù)中分離出噪聲、系統(tǒng)誤差、不同影響因素引起的變形或不同特征的變形信息是變形分析的一項主要工作,可為變形預(yù)報或工程結(jié)構(gòu)損傷狀態(tài)識別提供必要的信息。獨立分量分析(Independent Component Analysis,簡稱ICA)是一種盲信號分離方法,在信號分離方面具有獨特的優(yōu)勢,可利用原信號的相互獨立特性從混合觀測信號中分離出原信號,該特性使得ICA可作為變形分析的有力工具。然而獨立分量分析要求輸入信號不小于輸出信號,而在單測點變形分析中通常只有一個通道的觀測數(shù)據(jù)。為此,本文將單通道ICA方法引入變形分析中,在對已有的單通道ICA算法進行比較分析的基礎(chǔ)上,深入研究了基于相空間重構(gòu)的單通道ICA算法,提出了基于增強經(jīng)驗?zāi)Ｊ椒纸獾膯瓮ǖ罆r變ICA算法,并利用這些算法進行變形監(jiān)測數(shù)據(jù)去噪、變形效應(yīng)分量的分解及變形建模。主要研究工作與成果如下： 1.研究了獨立分量分析的基本理論研究,總結(jié)分析了FastICA算法、多維獨立分量分析等幾種常用的獨立分量分析算法。 2.通過無噪和有噪模擬信號實驗比較分析了動態(tài)嵌入ICA(DE-ICA)、小波ICA (WT-ICA)算法、經(jīng)驗?zāi)Ｊ椒纸釯CA (EMD-ICA)和增強經(jīng)驗?zāi)Ｊ椒纸釯CA(EEMD-ICA)四種單通道ICA算法,結(jié)果表明：DE-ICA算法的信號分離效果最優(yōu)；EMD-ICA算法最為簡單,無需任何參數(shù)設(shè)置；WT-ICA算法和DE-ICA算法速度較快,EMD-ICA算法其次,而EEMD-ICA算法由于多次迭代運行效率最低。 3.利用相空間重構(gòu)方法選取合適的嵌入維數(shù)和時間延遲,解決DE-ICA中嵌入維數(shù)過多會導(dǎo)致信號冗余和難以選取的問題,形成了基于相空間重構(gòu)的單通道ICA算法(PSR-ICA)。模擬實驗驗證了相空間重構(gòu)的單通道ICA算法不僅可以數(shù)據(jù)去噪,而且可以有效地分離觀測信號中的獨立信號。 4.利用PSR-ICA對五強溪大壩位移監(jiān)測數(shù)據(jù)進行了分析,首先利用PSR-ICA對位移監(jiān)測數(shù)據(jù)去噪,然后對利用該方法分離的位移分量與溫度、水位等影響因素進行關(guān)聯(lián)分析。結(jié)果表明：其中兩個提取的位移分量分別與溫度和水位高度相關(guān),與利用大壩統(tǒng)計位移模型計算的溫度和水位分量也基本一致,說明了PSR-ICA可有效地分離出大壩位移的各效應(yīng)分量。 5.研究了在線盲源分離的ICA算法——EASI算法,該算法可用于識別混合矩陣緩慢變化,具備FastICA這類離線算法所沒有的時變處理能力。模擬實驗驗證了EASI算法在多通道的時不變、突變和時變混合情況下的良好分離效果。提出了基于EEMD的單通道EASI算法(EEMD-EASI),并將其應(yīng)用于五強溪大壩位移監(jiān)測數(shù)據(jù)的單通道時變ICA分離中,結(jié)果表明:EEMD-ICA能夠更準(zhǔn)確地分離出溫度效應(yīng)位移分量及時效分量,從時變混合矩陣可以看出大壩蓄水幾年后趨于穩(wěn)定。
[Abstract]:The main work of deformation analysis is to separate out the deformation information caused by noise, systematic error, different influencing factors or different characteristics from the deformation monitoring data. It can provide necessary information for deformation prediction or damage state identification of engineering structures. Independent component Analysis (Independent Component Analysis,) is a blind signal separation method, which has a unique advantage in signal separation. The original signal can be separated from mixed observation signal by using the independent characteristics of the original signal. This feature makes ICA a powerful tool for deformation analysis. However, independent component analysis (ICA) requires that the input signal is not less than the output signal, but in the deformation analysis of a single measurement point, there is usually only one channel observation data. In this paper, the single-channel ICA method is introduced into the deformation analysis. Based on the comparison and analysis of the existing single-channel ICA algorithm, the single-channel ICA algorithm based on phase space reconstruction is deeply studied. A single channel time-varying ICA algorithm based on enhanced empirical mode decomposition is proposed, and these algorithms are used to Denoise the deformation monitoring data, decompose the deformation effect components and model the deformation. The main research work and results are as follows: 1. The basic theory of independent component analysis (ICA) is studied, and several common independent component analysis (ICA) algorithms, such as FastICA algorithm and multidimensional ICA algorithm, are summarized and analyzed. 2. Four single channel ICA algorithms, including dynamic embedded ICA (DE-ICA), wavelet ICA (WT-ICA), empirical mode decomposition (EMD-ICA) and enhanced empirical mode decomposition (ICA (EEMD-ICA), are compared with each other in noise-free and noise-free analog signal experiments. The results show that the DE-ICA algorithm has the best signal separation effect. The EMD-ICA algorithm is the simplest without any parameter setting. The WT-ICA algorithm and the DE-ICA algorithm are faster than the EMD-ICA algorithm, while the EEMD-ICA algorithm is the least efficient because of multiple iterations. 3. The phase space reconstruction method is used to select the appropriate embedding dimension and time delay to solve the problem that too many embedded dimensions in DE-ICA will lead to signal redundancy and difficult to select. A single channel ICA algorithm (PSR-ICA) based on phase space reconstruction is formed. The simulation results show that the single-channel ICA algorithm for phase space reconstruction can not only de-noise the data, but also effectively separate the independent signals from the observed signals. 4. The displacement monitoring data of Wuqiangxi dam are analyzed by PSR-ICA. Firstly, the displacement monitoring data are de-noised by PSR-ICA, then the displacements separated by this method are correlated with temperature, water level and other influencing factors. The results show that two of the extracted displacement components are related to temperature and water level respectively, and are consistent with the temperature and water level components calculated by using the dam statistical displacement model. It is shown that PSR-ICA can effectively separate all effect components of dam displacement. 5. In this paper, the ICA algorithm of online blind source separation, EASI algorithm, is studied. This algorithm can be used to identify the slow change of the mixed matrix, and has the ability of time-varying processing without the off-line algorithm such as FastICA. The simulation results show that the EASI algorithm has good separation performance in the case of multi-channel time-invariant, abrupt and time-varying mixing. A single channel EASI algorithm (EEMD-EASI) based on EEMD is proposed and applied to the separation of single channel time-varying ICA from the displacement monitoring data of Wuqiangxi Dam. The results show that EEMD-ICA can more accurately separate the temperature effect displacement component and the aging component, and from the time-varying mixing matrix, it can be seen that the dam tends to be stable after several years of water storage.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU196.1
本文編號：2400279
[Abstract]:The main work of deformation analysis is to separate out the deformation information caused by noise, systematic error, different influencing factors or different characteristics from the deformation monitoring data. It can provide necessary information for deformation prediction or damage state identification of engineering structures. Independent component Analysis (Independent Component Analysis,) is a blind signal separation method, which has a unique advantage in signal separation. The original signal can be separated from mixed observation signal by using the independent characteristics of the original signal. This feature makes ICA a powerful tool for deformation analysis. However, independent component analysis (ICA) requires that the input signal is not less than the output signal, but in the deformation analysis of a single measurement point, there is usually only one channel observation data. In this paper, the single-channel ICA method is introduced into the deformation analysis. Based on the comparison and analysis of the existing single-channel ICA algorithm, the single-channel ICA algorithm based on phase space reconstruction is deeply studied. A single channel time-varying ICA algorithm based on enhanced empirical mode decomposition is proposed, and these algorithms are used to Denoise the deformation monitoring data, decompose the deformation effect components and model the deformation. The main research work and results are as follows: 1. The basic theory of independent component analysis (ICA) is studied, and several common independent component analysis (ICA) algorithms, such as FastICA algorithm and multidimensional ICA algorithm, are summarized and analyzed. 2. Four single channel ICA algorithms, including dynamic embedded ICA (DE-ICA), wavelet ICA (WT-ICA), empirical mode decomposition (EMD-ICA) and enhanced empirical mode decomposition (ICA (EEMD-ICA), are compared with each other in noise-free and noise-free analog signal experiments. The results show that the DE-ICA algorithm has the best signal separation effect. The EMD-ICA algorithm is the simplest without any parameter setting. The WT-ICA algorithm and the DE-ICA algorithm are faster than the EMD-ICA algorithm, while the EEMD-ICA algorithm is the least efficient because of multiple iterations. 3. The phase space reconstruction method is used to select the appropriate embedding dimension and time delay to solve the problem that too many embedded dimensions in DE-ICA will lead to signal redundancy and difficult to select. A single channel ICA algorithm (PSR-ICA) based on phase space reconstruction is formed. The simulation results show that the single-channel ICA algorithm for phase space reconstruction can not only de-noise the data, but also effectively separate the independent signals from the observed signals. 4. The displacement monitoring data of Wuqiangxi dam are analyzed by PSR-ICA. Firstly, the displacement monitoring data are de-noised by PSR-ICA, then the displacements separated by this method are correlated with temperature, water level and other influencing factors. The results show that two of the extracted displacement components are related to temperature and water level respectively, and are consistent with the temperature and water level components calculated by using the dam statistical displacement model. It is shown that PSR-ICA can effectively separate all effect components of dam displacement. 5. In this paper, the ICA algorithm of online blind source separation, EASI algorithm, is studied. This algorithm can be used to identify the slow change of the mixed matrix, and has the ability of time-varying processing without the off-line algorithm such as FastICA. The simulation results show that the EASI algorithm has good separation performance in the case of multi-channel time-invariant, abrupt and time-varying mixing. A single channel EASI algorithm (EEMD-EASI) based on EEMD is proposed and applied to the separation of single channel time-varying ICA from the displacement monitoring data of Wuqiangxi Dam. The results show that EEMD-ICA can more accurately separate the temperature effect displacement component and the aging component, and from the time-varying mixing matrix, it can be seen that the dam tends to be stable after several years of water storage.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU196.1
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