【作者】 王娟；

【导师】 杨杰；

【作者基本信息】 西安理工大学 ， 水工结构工程， 2017， 硕士

【摘要】 大坝安全是社会、国防和生态安全的重中之重。对大坝进行安全监测,及时掌握大坝的安全性态及演变趋势是确保大坝安全的重要手段。随着大坝自动化监测系统的发展,数据量呈几何性增长,数据规模不断扩大,如何高效科学地处理分析这些数据成为新的研究热点。本文以海量数据处理分析需求为出发点,以大坝整体性分析为主要思路,利用盲源分离理论以及相关向量机的相关方法对大坝监测数据进行研究分析,力求寻找科学、可靠、高效的监测数据处理方法。本文的主要研究工作如下:研究了大坝缺失监测数据的插值方法,提出了基于核独立分量分析-相关向量机(KICA-RVM)的大坝缺失监测数据插值方法。根据大坝各监测点间数据的相关性提出了利用相关测点为目标测点估值的大坝缺失监测数据插值方法。该方法利用核独立分量分析方法提取相关测点中统计独立的特征信息,通过相关向量机对目标测点进行回归预测。该方法具有精度高、适应性强、便于操作等优点。为了提高监测数据分析的效率,建立了基于快速独立分量分析-相关向量机(Fast ICA-RVM)的多测点监测模型。利用快速独立分量分析算法提取多测点的特征信息,并且运用该方法对预报影响因子进行降维去除自变量因子间的相关性,然后利用相关向量机良好的函数逼近能力对多测点特征信息进行回归预测,实例验证表明该模型能够达到单测点模型同水平的预测精度。同时,由于相关向量机不但能够输出确定性预测结果而且能够输出预测值的概率分布,给出一定置信度的预测区间,便于指导监测系统的决策。针对水工建筑物裂缝开度等监测物理量的监测资料一般是非平稳的特点,建立了基于时频分布盲源分离-多输出相关向量机(TFBSS-MRVM)的大坝多测点时变监测模型。利用能够有效提取非平稳信号时变特征的时频分布盲源分离算法提取多测点的非平稳特征信息。同时,利用多输出相关向量机对多个特征信息同时进行预测,进一步提高监测效率。经实例验证,该模型对于裂缝开度等非平稳监测量的预测具有较高精度。更多还原

【Abstract】 Dam safety is the top priority of social,national defense and ecological security.It is an important means to ensure the safety of the dam by carrying out the safety monitoring of the dam and grasping the safety state and the development trend of the dam.With the development of dam automatic monitoring system,the amount of data is growing geometrically and the data scale is expanding.How to deal with these data efficiently and scientifically becomes a new research hotspot.Based on the massive data processing and analysis demand as the starting point,dam integrity analysis as the main train of thought,research and analysis of dam monitoring data using related method of blind source separation theory and Relevance vector machine,in order to find scientific,reliable and efficient monitoring data processing method.The main research work of this paper is as follows:This paper studies the interpolation method of dam missing monitoring data,and puts forward a method based on Kernel Independent Component Analysis and relevance vector machine.According to the correlation between the monitoring data of the dam,this paper puts forward a method of dam missing monitoring data interpolation,in which method the relevant measured points are used to estimate the target points.The kernel independent component analysis(ICA)is used to extract the statistical independent feature information from the relevant measured points.The method has the advantages of high precision,strong adaptability and easy operation.In order to improve the efficiency of monitoring data analysis,a multi-point monitoring model based on fast independent component analysis and relevance vector machine(FastICA-RVM)was established.Fast independent component analysis algorithm is utilized to extract the characteristic information of the multipoint,and the algorithm is applied to remove the correlation dimension among independent variable factors for forecasts impact factor,and then use the good function approximation ability of the relevance vector machine to predict the characteristics of multi measured points.The simulation results show that the model can achieve the same prediction accuracy level of single point model.At the same time,because the relevance vector machine can not only output the deterministic forecasting results,but also can output the probability distribution of the predicted value,the prediction interval under a certain confidence level is given,which is helpful to guide the decision-making of the monitoring system.For the monitoring data of the physical quantities such as the crack opening of the hydraulic structures are generally non-stationary,a multi-point time-varying dam monitoring model based on time-frequency distribution blind source separation and multiple output relevance vector machine(TFBSS-MRVM)was established.Non-stationary feature information of multi measured points was extracted by the time-frequency distribution blind source separation algorithm which can effectively extract non-stationary signal time-varying feature.And multi-output relevance vector machine is used to predict multiple characteristic information simultaneously,which can further improve the monitoring efficiency.The example shows that the model has high accuracy for the prediction of the crack opening and other non-stationary monitoring.更多还原