【摘要】：橋梁結(jié)構(gòu)健康監(jiān)測(cè)對(duì)于橋梁結(jié)構(gòu)的正常使用以及人民生命財(cái)產(chǎn)的安全具有重大意義。GPS監(jiān)測(cè)是橋梁健康監(jiān)測(cè)的重要手段。隨著GPS技術(shù)的發(fā)展,目前已經(jīng)能夠?qū)崿F(xiàn)實(shí)時(shí)動(dòng)態(tài)監(jiān)測(cè)功能。因此,GPS監(jiān)測(cè)信號(hào)隱含了更加豐富的結(jié)構(gòu)健康信息有待挖掘。本文以鶴洞大橋健康監(jiān)測(cè)系統(tǒng)為依托,以GPS監(jiān)測(cè)信號(hào)為對(duì)象,以識(shí)別橋梁結(jié)構(gòu)的自振頻率為目的,展開(kāi)以下研究工作:(1)信號(hào)分解方法。改進(jìn)HHT方法(課題組前期研究成果)采用具有預(yù)測(cè)性的Kriging擬合代替三次樣條擬合技術(shù)進(jìn)行HHT分解,能有效的改善HHT方法存在的端點(diǎn)效應(yīng)和模態(tài)混疊現(xiàn)象。但HHT分解效果很大程度上取決于Kriging擬合過(guò)程中相關(guān)模型參數(shù)?的初始取值。為此,本文采用尋優(yōu)能力較強(qiáng)的粒子群算法(PSO)對(duì)參數(shù)?的取值進(jìn)行尋優(yōu),通過(guò)尋優(yōu)過(guò)程消除參數(shù)?初始取值對(duì)改進(jìn)HHT分析效果的影響。對(duì)正弦、時(shí)變Chirp疊加信號(hào)進(jìn)行分析,結(jié)果表明,增加參數(shù)?優(yōu)化過(guò)程的改進(jìn)HHT方法(以下簡(jiǎn)稱優(yōu)化K-HHT,其EMD過(guò)程稱為優(yōu)化K-EMD過(guò)程)分解出更趨于實(shí)際情況的IMF分量(固有模態(tài)函數(shù)),并且能夠有效的控制HHT方法的端點(diǎn)效應(yīng)問(wèn)題。(2)信號(hào)趨勢(shì)項(xiàng)的分離。要從GPS信號(hào)中識(shí)別出結(jié)構(gòu)的自振頻率,必須分離GPS信號(hào)中的多路徑效應(yīng)和荷載作用下的結(jié)構(gòu)位移,即GPS信號(hào)的趨勢(shì)項(xiàng)。采用最小二乘法、小波變換、優(yōu)化K-HHT分離數(shù)字仿真信號(hào)中的趨勢(shì)項(xiàng)。以剔除趨勢(shì)前后信號(hào)的方差、均值、相關(guān)系數(shù)以及分離的趨勢(shì)與真值的均方根誤差為評(píng)價(jià)指標(biāo),對(duì)比三種方法對(duì)趨勢(shì)項(xiàng)的分離效果。結(jié)果表明優(yōu)化K-HHT效果最佳。(3)信號(hào)降噪處理。以均方根誤差、歸一化絕對(duì)誤差、信噪比以及系統(tǒng)平均偏差作為評(píng)價(jià)指標(biāo),對(duì)比優(yōu)化K-HHT方法、小波變換、優(yōu)化K-HHT-Wavelet三種方法的降噪效果。仿真算例表明,優(yōu)化K-HHT-Wavelet方法對(duì)非平穩(wěn)信號(hào)降噪的效果要優(yōu)于其他兩種方法,并且降噪后的信號(hào)曲線更加平滑。(4)基于上述成果分析鶴洞大橋GPS監(jiān)測(cè)信號(hào)。首先用優(yōu)化K-HHT分離趨勢(shì)項(xiàng),然后利用優(yōu)化K-HHT-Wavelet方法進(jìn)行降噪,獲得鶴洞大橋振動(dòng)位移時(shí)程,從而識(shí)別出橋梁的自振頻率。該頻率與理論計(jì)算值及加速度時(shí)程分析結(jié)果非常接近。
[Abstract]:The health monitoring of bridge structure is of great significance for the normal use of bridge structure and the safety of people's life and property. GPS monitoring is an important means of bridge health monitoring. With the development of GPS technology, real-time dynamic monitoring has been realized. Therefore, GPS monitoring signals imply more abundant structural health information to be mined. Based on the health monitoring system of Hedong Bridge, this paper takes the GPS monitoring signal as the object, and aims at identifying the natural vibration frequency of the bridge structure. The following research work is carried out: (1) signal decomposition method. Using predictive Kriging fitting instead of cubic spline fitting to decompose HHT, the improved HHT method can effectively improve the endpoint effect and modal aliasing in HHT method. But the effect of HHT decomposition largely depends on the model parameters in the process of Kriging fitting. The initial value of. In this paper, a particle swarm optimization algorithm, (PSO), is used to match the parameters. The parameters are eliminated by the optimization process. The effect of initial value on the effect of improved HHT analysis. The sinusoidal and time-varying Chirp superposition signals are analyzed. The results show that the parameters are increased. The improved HHT method for optimization process (hereinafter referred to as optimized K-HHT, whose EMD process is called optimized K-EMD process) decomposes the more practical IMF component (intrinsic mode function). And it can effectively control the endpoint effect of HHT method. (2) the separation of signal trend term. In order to identify the natural frequency of the structure from the GPS signal, it is necessary to separate the multipath effect in the GPS signal and the displacement of the structure under load, that is, the trend term of the GPS signal. Using the least square method and wavelet transform, the trend term of digital simulation signal separated by K-HHT is optimized. Taking the variance, mean value, correlation coefficient of the signal before and after the elimination of the trend, and the root mean square error of the separating trend and the true value as the evaluation indexes, the separation effect of the three methods on the trend term is compared. The results show that the optimization of K-HHT is the best. (3) signal noise reduction. The root-mean-square error, normalized absolute error, signal-to-noise ratio (SNR) and average deviation of the system are taken as evaluation indexes, and the noise reduction effects of the three methods, such as optimized K-HHT method, wavelet transform and K-HHT-Wavelet method, are compared and optimized. The simulation results show that the optimal K-HHT-Wavelet method is better than the other two methods in reducing the noise of non-stationary signals, and the signal curve is smoother after the noise reduction. (4) based on the above results, the GPS monitoring signals of Hedong Bridge are analyzed. The vibration displacement time history of Hedong Bridge is obtained by optimizing the K-HHT separation trend term, and then using the optimized K-HHT-Wavelet method to reduce the noise, so as to identify the natural vibration frequency of the bridge. The frequency is very close to the theoretical calculation value and the acceleration time history analysis result.
【學(xué)位授予單位】：廣州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U446

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 韓建平;李林;王洪濤;錢炯;;基于Hilbert-Huang變換和隨機(jī)減量技術(shù)的模態(tài)參數(shù)識(shí)別[J];世界地震工程;2011年01期

2 王超;沈斐敏;;小波變換在探地雷達(dá)弱信號(hào)去噪中的研究[J];物探與化探;2015年02期

相關(guān)碩士學(xué)位論文 前1條

1 趙寶新;車載運(yùn)輸系統(tǒng)激勵(lì)載荷分析處理技術(shù)研究[D];中北大學(xué);2009年

，

本文編號(hào)：2309057