优化K-HHT方法及其在GPS数据处理中的应用
发布时间：2018-11-03 21:14
【摘要】：桥梁结构健康监测对于桥梁结构的正常使用以及人民生命财产的安全具有重大意义。GPS监测是桥梁健康监测的重要手段。随着GPS技术的发展,目前已经能够实现实时动态监测功能。因此,GPS监测信号隐含了更加丰富的结构健康信息有待挖掘。本文以鹤洞大桥健康监测系统为依托,以GPS监测信号为对象,以识别桥梁结构的自振频率为目的,展开以下研究工作:(1)信号分解方法。改进HHT方法(课题组前期研究成果)采用具有预测性的Kriging拟合代替三次样条拟合技术进行HHT分解,能有效的改善HHT方法存在的端点效应和模态混叠现象。但HHT分解效果很大程度上取决于Kriging拟合过程中相关模型参数?的初始取值。为此,本文采用寻优能力较强的粒子群算法(PSO)对参数?的取值进行寻优,通过寻优过程消除参数?初始取值对改进HHT分析效果的影响。对正弦、时变Chirp叠加信号进行分析,结果表明,增加参数?优化过程的改进HHT方法(以下简称优化K-HHT,其EMD过程称为优化K-EMD过程)分解出更趋于实际情况的IMF分量(固有模态函数),并且能够有效的控制HHT方法的端点效应问题。(2)信号趋势项的分离。要从GPS信号中识别出结构的自振频率,必须分离GPS信号中的多路径效应和荷载作用下的结构位移,即GPS信号的趋势项。采用最小二乘法、小波变换、优化K-HHT分离数字仿真信号中的趋势项。以剔除趋势前后信号的方差、均值、相关系数以及分离的趋势与真值的均方根误差为评价指标,对比三种方法对趋势项的分离效果。结果表明优化K-HHT效果最佳。(3)信号降噪处理。以均方根误差、归一化绝对误差、信噪比以及系统平均偏差作为评价指标,对比优化K-HHT方法、小波变换、优化K-HHT-Wavelet三种方法的降噪效果。仿真算例表明,优化K-HHT-Wavelet方法对非平稳信号降噪的效果要优于其他两种方法,并且降噪后的信号曲线更加平滑。(4)基于上述成果分析鹤洞大桥GPS监测信号。首先用优化K-HHT分离趋势项,然后利用优化K-HHT-Wavelet方法进行降噪,获得鹤洞大桥振动位移时程,从而识别出桥梁的自振频率。该频率与理论计算值及加速度时程分析结果非常接近。
[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.
【学位授予单位】：广州大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U446
本文编号：2309057
[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.
【学位授予单位】：广州大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U446
【参考文献】
相关期刊论文 前2条
1 韩建平;李林;王洪涛;钱炯;;基于Hilbert-Huang变换和随机减量技术的模态参数识别[J];世界地震工程;2011年01期
2 王超;沈斐敏;;小波变换在探地雷达弱信号去噪中的研究[J];物探与化探;2015年02期
相关硕士学位论文 前1条
1 赵宝新;车载运输系统激励载荷分析处理技术研究[D];中北大学;2009年
，本文编号：2309057
本文链接：https://www.wllwen.com/kejilunwen/daoluqiaoliang/2309057.html
