提升小波多尺度分解法与RBF神经网络在混凝土重力坝应力监测中的应用  

Application of Lifting Wavelet Multi-scale Decomposition Method and RBF Neural Network in Stress Monitoring of Concrete Gravity Dam

作　　者：赵鲲鹏[1] 赵二峰[1] 王莹[2] 张毅[3] 

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098 [2]南京水利科学研究院水工水力学研究所,江苏南京210029 [3]黄河上游水电开发有限责任公司,青海西宁810008

出　　处：《水力发电》2015年第2期81-85,共5页Water Power

基　　金：国家自然科学基金重点项目(51139001);国家自然科学基金青年科学基金项目(51209077);水利部公益性行业科研专项经费项目(201201038;201301061);江苏高校优势学科建设工程资助项目(水利工程)(YS 11001)

摘　　要：将混凝土坝的应力监测数据视为一组含有不同频率信号的时间序列,针对信号中的低频信号和高频信号,采用提升小波的多尺度分解法提取混凝土坝应力的时效分量、水压和温度分量以及噪声分量,采用计算信噪比极大值的方法提取周期性分量中的水压、温度分量和噪声分量,得到最优的去噪效果。使用RBF神经网络对去噪信号进行建模,预测结果表明,该模型能够很好地反映混凝土坝应力变化的趋势和规律,可应用于混凝土坝安全监测中。The observation data of concrete dam stress is regarded as a set of time series which contains different frequency signals. Aiming at the signal with high frequency or low frequency, a new method of lifting wavelet multi-scale decomposition is proposed to extract the time dependent component, the water pressure and temperature component as well as the noise component from the observation date of dam stress, and then the method of calculating maximum SNR is used to extract the components of water pressure and temperature and noise component from cyclical component, which can get the most optimal denoising effect. The RBF neural network is finally used to model the denoising signals for stress prediction. The prediction results show that the model can well reflect the trends and patterns of stress changes of concrete dam.

关 键 词：提升小波多尺度分解法 RBF神经网络 应力监测 混凝土重力坝 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TV642.3[自动化与计算机技术—控制科学与工程]