贝叶斯方法在大跨度斜拉桥模态参数识别中的应用研究

为研究大跨度斜拉桥模态参数的不确定性,将遗传算法引入传统快速贝叶斯傅里叶变换法中,并采用高信噪比渐进估计值约束遗传算法的参数搜索空间,发展了一种大跨度桥梁的贝叶斯模态参数识别方法。利用悬臂梁数值模拟验证该方法的识别效率与精度;依托苏通大桥实测加速度数据应用上述方法开展大跨度斜拉桥的模态参数识别。在此基础上,探讨频带宽度系数对模态参数识别精度和不确定性的影响,并分析模态参数后验概率密度函数（PDF）的分布特征。结果表明,所提方法可有效识别大跨度斜拉桥的各阶模态参数;频率和振型的不确定性较低,而阻尼比的不确定性较高;将频带宽度系数限制在5～10有利于保证识别误差与不确定性的平衡;模态参数后验PDF符合高斯分布。     

识别结构模态参数的神经网络方法研究

提出了在输入未知时基于频率呼应函数用神经网络识别结构模态参数的方法,并研究了不同的噪声水平和网络输入层点数目对网络输出误差的影响。讨论了网络对不同阶数模态参数的识别精度。数值结果表明此方法是可行的。     

基于贝叶斯功率谱变量分离方法的实桥模态参数识别

工程结构参数识别不可避免地受到测试噪声和模型不确定性的影响,因此在模态参数识别过程中引入贝叶斯方法进行不确定性量化,具有较为重要的意义。通过对自功率谱和互功率谱的统计特性进行分析表明,功率谱迹（自功率谱之和）的概率密度函数与振型无关,因此可以实现振型参数与其它参数（频率、阻尼比、模态激励和预测误差等）的分离。以此变量分离原理为依据,可以实现\     

桥梁车辆荷载识别的贝叶斯方法研究

基于贝叶斯推理提出了一种可实现误差模式选择的桥梁车辆荷载识别方法。该方法通过静力影响线构建车辆荷载与实测响应的关系表达式,并建立修正曲面以消除动力效应造成的识别误差。引入与结构响应大小和车速相关的五种误差模式。根据假设的先验分布推导车辆轴重参数的后验分布,以获得车辆荷载的最优估计值和置信区间,并计算各误差模式的后验概率。分别采用简支梁数值算例和某连续梁桥动载试验,对该方法在不同车速工况下的识别精度和可靠性进行了验证。结果表明,修正曲面可以有效消除车辆动力冲击的影响,提高了荷载识别精度;荷载识别结果以置信区间形式呈现,可量化荷载识别结果的不确定性;贝叶斯方法能够识别出最佳误差模式,进一步提升了荷载识别的鲁棒性。     

基于贝叶斯估计的动载荷识别方法

为了降低测量误差等不确定性因素对识别结果的影响,建立基于贝叶斯估计理论的动力学系统载荷识别方法。首先,根据动力学系统运动方程,利用贝叶斯理论,推导载荷和误差参数的联合后验分布,进而得到载荷和误差参数的边缘概率分布;然后,采用马尔可夫蒙特卡罗方法,估计动力学系统所受的载荷,并利用仿真算例与基于奇异值分解的载荷识别方法进行对比;最后,利用实验数据,进一步验证本方法的有效性。结果表明,该方法在一定程度上减小了不确定性因素造成的识别误差,对于提高动载荷识别精度具有一定的参考意义。     

消能减震建筑结构模态参数识别的贝叶斯方法

为研究消能减震建筑结构中阻尼器的附加阻尼和刚度贡献,建议一种基于贝叶斯统计推断的模态参数识别方法,可用于定量估计阻尼器对结构模态参数的影响,包括阻尼比、频率和振型,以及参数的估计不确定性.为精确建立模态参数与质量和刚度矩阵的函数关系,采用直接模型修正技术进行模型参数化建模,利用模态参数以解析方式重构结构质量、刚度和阻尼...     

环境激励下基于快速贝叶斯FFT的实桥模态参数识别

近年来,贝叶斯理论逐步应用于工程结构的模态参数识别、有限元模型修正及状态评估等方面。基于快速贝叶斯FFT的模态参数识别方法是针对某一共振频率带的单个模态,通过一个四维的数值优化问题得到模态参数的最佳估计,并通过对数似然函数关于模态参数的二阶导数求得Hessian矩阵,使得基于贝叶斯的参数识别方法可以快速高效地进行。为了评估该方法在实际桥梁结构模态参数识别应用中的可行性及优越性,运用快速贝叶斯FFT方法对环境激励下一刚构-连续组合梁桥的竖向加速度响应进行了分析处理,识别了其模态参数的最佳估计,并根据模态参数的变异系数评估了其后验的不确定性。识别结果与随机子空间识别结果的对比表明,两种方法识别的频率和振型基本吻合,阻尼识别结果的差异仍然较大。     

结构物理参数识别的贝叶斯估计马尔可夫蒙特卡罗方法

从结构动力特征方程出发,以结构主模态参数为观测量,推得结构物理参数线性回归模型。对该模型应用贝叶斯估计理论得到物理参数后验联合分布,再结合马尔可夫蒙特卡罗抽样方法给出各个物理参数的边缘概率分布和最优估计值,而提出了基于结构主模态参数的结构物理参数识别贝叶斯估计马尔可夫蒙特卡罗方法。对五层剪切型结构的数值研究表明,此方法能够利用少数主模态参数给出结构质量和刚度参数的概率分布和最优识别值,而且在主模态参数较准确时识别误差很小。     

基于模态分析的特定结构状态监测方法

模态分析技术已经广泛应用于工业生产和研究领域,系统参数识别与故障诊断已经成为重要的技术.在国际军控核查活动中,对特定结构状态的核查监测是非常重要的技术环节.特别是对重要的设施或结构,在封存控制期间是否发生未经许可的启用或拆卸转移,需要作出快速监测.提出了基于模态试验和分析的监测方法,并针对特定结构进行了原理试验,获得了比较理想的试验数据.试验结果表明,利用模态分析方法可以实现特定结构状态的监测,与其它方法相比,设备简单,重复性好,入侵性小,是值得推荐的未来军控核查手段.     

基于希尔伯特变换结构模态参数识别

应用HHT方法对GARTEUR飞机模型模态参数进行识别,通过采用多通带滤波器对信号进行滤波,较好的解决模态混叠问题,采用NExT法对信号预处理,由EMD分解获得较准确的各阶固有模态函数分量（IMF）,在EMD分解中使用镜像延拓方法对极值点进行处理来抑制端点效应,然后将分解得到的IMF分量进行希尔伯特变换并结合ITD法识别出各阶固有频率和阻尼比。最后对悬臂梁进行数值仿真模拟,并将模态参数识别结果和理论值进行对比,并运用此方法进一步识别GARTEUR飞机模型固有模态参数。     

A Bayesian Approach to Multiple Comparisons

This paper presents maximum likelihood theory for large-sample optimum accelerated life test plans. The plans are used to estimate a simple linear relationship between (transformed) stress and product life, which has a Weibull or smallest extreme value distribution. Censored data are to be analyzed before all test units fail. The plans show that all test units need not run to failure and that more units should be tested at low test stresses than at high ones. The plans are illustrated with a voltage-accelerated life test of an electrical insulating fluid.     

多重模糊假设检验的贝叶斯方法

应用统计推断和统计决策问题中,常常需要处理模糊概念,模糊假设检验是其中非常重要的一种情形。本文将模糊性引入到多重假设中,从贝叶斯决策的角度,分样本为清晰数据和模糊信息两种情形,研究了多重模糊假设的贝叶斯方法,并给出了数值例。     

A Bayesian Approach to Capability Testing Based on Cpk with Multiple Samples

Process capability indices provide numerical measures to compare the output of a process to client's expectations. However, most of the existing researches have used traditional distribution frequency method by using a single sample due to assess process capability. An alternative to this approach is to use the Bayesian method. In this paper, we utilize a Bayesian approach based on subsamples to check process capability via capability index C_pk. As a new suggestion, we used the informative normal prior distribution and the characteristics of sufficient statistic of the parameter to drive the posterior distribution. The capability test is done, and the posterior probability p, for which the process under investigation is capable, is derived both based on the most popular index C_pk. Finally, a numerical example is given to clarify the method. Copyright © 2013 John Wiley & Sons, Ltd.     

A Distribution-Free Bayesian Approach for Determining the Joint Probability of Failure of Materials Subject to Multiple Proof Loads

Survival of manufacturing materials or component parts may depend on two or more variables, and yet joint strength tests can be difficult to perform. For many such materials, multiple strength properties can be estimated only using destructive testing. This problem gives rise to a technique called proof loading—stressing units up to a prescribed load, destroying only the weaker units, and leaving the survivors for further tests. We propose a distribution-free Bayesian approach for estimating the probability of joint failure under two proof loads. Our method does not assume that proof-load survivors are undamaged.     

A Bayesian Approach to Condition Monitoring with Imperfect Inspections

Degradation is a common phenomenon for many products. Because of a variety of reasons, the degradation rates of units from the same population are often heterogeneous. In addition, when the degradation process is monitored using dedicated sensors, the measurements are often inaccurate because of various noisy factors. To account for the heterogeneous degradation rate and the non‐negligible measurement errors, we model the degradation observations using a random‐effects Wiener process with measurement errors. Under the model, direct estimation of current degradation and prediction of future degradation are difficult. We thus develop a filtering algorithm that recursively estimates the joint distribution of the degradation rate and the current degradation levels. Based on the estimates, the distribution of the remaining useful life can be timely predicted. Our method is both computational efficient and storage efficient. Its effectiveness is demonstrated through simulation and real data. Copyright © 2013 John Wiley & Sons, Ltd.     

A Recommendation Approach Based on Bayesian Networks for Clone Refactor

Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are identified, and the static and evolutions features are extracted to build the feature database. Finally, the Bayesian network classifier is used for training and evaluating the classification results. Based on more than 640 refactor examples of five open source software developed in C, we observe a considerable enhancement. The results show that the accuracy of the approach is larger than 90%. We believe our approach will provide a more accurate and reasonable code refactoring and maintenance advice for software developers.     

形状修改结构动特性快速分析的边界元摄动法

本文提出了一种用于结构特征值和特征矢量快速分析的边界元摄动法.该方法把结构的形状改变看作其边界节点坐标在某一确定值邻域内的摄动,利用以边界元为基础的摄动理论,得到修改后结构的特征对,避免了重复求解广义特征值问题.文中的算例证明了本方法的有效性.     

A Bayesian Variable-Selection Approach for Analyzing Designed Experiments With Complex Aliasing

Experiments using designs with complex aliasing patterns are often performed—for example, twolevel nongeometric Plackett-Burman designs, multilevel and mixed-level fractional factorial designs, two-level fractional factorial designs with hard-to-control factors, and supersaturated designs. Hamada and Wu proposed an iterative guided stepwise regression strategy for analyzing the data from such designs that allows entertainment of interactions. Their strategy provides a restricted search in a rather large model space, however. This article provides an efficient methodology based on a Bayesian variable-selection algorithm for searching the model space more thoroughly. We show how the use of hierarchical priors provides a flexible and powerful way to focus the search on a reasonable class of models. The proposed methodology is demonstrated with four examples, three of which come from actual industrial experiments.