Damage localization of marine risers using time series of vibration signals

Based on dynamic response signals a damage detection algorithm is developed for marine risers. Damage detection methods based on numerous modal properties have encountered issues in the researches in offshore oil community. For example, significant increase in structure mass due to marine plant/animal growth and changes in modal properties by equipment noise are not the result of damage for riser structures. In an attempt to eliminate the need to determine modal parameters, a data-based method is developed. The implementation of the method requires that vibration data are first standardized to remove the influence of different loading conditions and the autoregressive moving average (ARMA) model is used to fit vibration response signals. In addition, a damage feature factor is introduced based on the autoregressive (AR) parameters. After that, the Euclidean distance between ARMA models is subtracted as a damage indicator for damage detection and localization and a top tensioned riser simulation model with different damage scenarios is analyzed using the proposed method with dynamic acceleration responses of a marine riser as sensor data. Finally, the influence of measured noise is analyzed. According to the damage localization results, the proposed method provides accurate damage locations of risers and is robust to overcome noise effect.     

Modal strain energy based structural damage localization for offshore platform using simulated and measured data

Modal strain energy based methods for damage detection have received much attention. However, most of published articles use numerical methods and some studies conduct modal tests with simple 1D or 2D structures to verify the damage detection algorithms. Only a few studies utilize modal testing data from 3D frame structures. Few studies conduct performance comparisons between two different modal strain energy based methods. The objective of this paper is to investigate and compare the effectiveness of a traditional modal strain energy method （Stubbs index） and a recently developed modal strain energy decomposition （MSED） method for damage localization, for such a purpose both simulated and measured data from an offshore platform model being used. Particularly, the mode shapes used in the damage localization are identified and synthesized from only two measurements of one damage scenario because of the limited number of sensors. The two methods were first briefly reviewed. Next, using a 3D offshore platform model, the damage detection algorithms were implemented with different levels of damage severities for both single damage and multiple damage cases. Finally, a physical model of an offshore steel platform was constructed for modal testing and for validat-ing the applicability. Results indicate that the MSED method outperforms the Stubbs index method for structural damage detection.     

2014-11-22康定MS6.3地震加速度记录的时频分析与震害特征

采用一种基于聚类经验模态分解（EEMD）的HHT方法提取康定M_S6.3地震强震记录的时频特性,通过对各台站获取的强震动加速度记录进行EEMD和Hilbert变换及谱分析,并与Fourier变换进行对比研究。结果表明,对于非线性的强震记录,采用EEMD能有效抑制EMD中存在的模态混叠问题。康定震区震害调查结果发现,“崩科”藏式建筑房屋表现出的震害特征较严重。通过Hilbert边际谱分析可以看出,震区台站主频集中在10 Hz左右,瞬时能量谱高频段信息较为丰富,而藏式建筑较差的抗震性也是造成震区“崩科”藏式结构房屋表现出震害偏重的重要原因。     

An Improved Lower Order Method of Modal Parameter Estimation for Offshore Structures Using Reconstructed Signals

For modal parameter estimation of offshore structures, one has to deal with two challenges: 1) identify the interested frequencies, and 2) reduce the number of false modes. In this article, we propose an improved method of modal parameter estimation by reconstructing a new signal only with interested frequencies. The approach consists of three steps: 1) isolation and reconstruction of interested frequencies using FFT filtering, 2) smoothness of reconstructed signals, and 3) extraction of interested modal parameters in time domain. The theoretical improvement is that the frequency response function(FRF) of filtered signals is smoothed based on singular value decomposition technique. The elimination of false modes is realized by reconstructing a block data matrix of the eigensystem realization algorithm(ERA) using the filtered and smoothed signals. The advantage is that the efficiency of the identification process of modal parameters will be improved greatly without introducing any false modes. A five-DOF mass-spring system is chosen to illustrate the procedure and demonstrate the performance of the proposed scheme. Numerical results indicate that interested frequencies can be isolated successfully using FFT filtering, and unexpected peaks in auto spectral density can be removed effectively. In addition, interested modal parameters, such as frequencies and damping ratios, can be identified properly by reconstructing the Hankel matrix with a small dimension of ERA, even the original signal has measurement noises.     

顾及时变非高斯噪声的高斯和滤波及其导航应用

高斯和滤波可利用高斯混合模型精化非高斯嗓声随机模型来提高估计精度,但导航测量环境的动态性和复杂性使非高斯噪声具有时变性特征,若GMM不随之调整会导致滤波解算失真.针对该问题,本文提出一种基于位移参数自适应估计的高斯和滤波算法.首先分析GMM位移参数对非高斯噪声拟合精度的影响,然后利用位移参数自适应技术修正GMM,进而改...     

顾及单木三维形态的无人机立体影像单木识别算法

单木参数对当前的森林资源管理、生态研究以及生物多样性保护等具有重要意义。无人机立体影像数据与单木识别算法为单木参数的低成本、自动化获取提供了基础。现有研究表明,常用的基于局部最大值搜索的单木识别算法面对密集林分时存在严重的漏识别问题,影响了参数提取的精度,因此本文提出了顾及单木三维形态的无人机立体影像单木识别新算法。算法首先综合利用无人机立体影像的高程与RGB光谱信息,通过随机森林分类进行林冠区的提取;然后利用形态学的多层腐蚀、膨胀与连通区标记进行树冠相连单木的分离与树冠中心点的提取,从而实现单木自动化识别。本文选取内蒙古大兴安岭林区和四川王朗林区的4块样地进行验证,以目视解译数据为参考,分别与基于高程值的局部最大值搜索算法（算法A）、基于RGB光谱亮度值的局部最大值搜索算法（算法B）进行比较。结果显示：本文提出的算法在4个样地的平均F1-score为94.17%,与算法A和算法B相比分别提高了15.85%和9.37%;而对于密集样地,本文提出的算法在查全率上相比算法A和算法B分别提高51.79%和35.64%。结果表明本文提出的算法在不同林区均能够实现较好的单木识别效果,特别是能够有效避免密集林分下的漏识别问题,为基于无人机立体影像的单木识别研究提供了一种新的思路。     

非线性不等式约束平差的一种新岭估计算法

利用先验信息对参数加以约束,建立非线性不等式约束平差模型,并提出一种新的岭估计算法解算该平差模型。2个病态算例实验证明了本文算法的有效性,且计算结果精度高于普通岭估计。     

An improved CHC algorithm for damage diagnosis of offshore platforms

1 Introduction Health monitoring of structures, especially large spacestructures intended for long-term operation, is essential indeveloping short-term or long-term repair plans. Becausethe maintenance, replacement, and time out of service arecostly, it i…     

基于双线偏振多普勒雷达识别地物回波

地物回波的存在严重影响雷达数据质量,从而影响定量降水估测精度。双线偏振多普勒天气雷达在识别地物回波方面具有明显优势。分析了北京市气象局C波段双线偏振多普勒天气雷达探测到的降水回波和地物回波的特征,在刘黎平的模糊逻辑算法基础上选择容易区分降水回波和地物回波的特征量,加入偏振参数构成偏振模糊逻辑算法。利用2012年的"7.21特大暴雨"个例对识别效果进行分析,并与常规模糊逻辑算法识别效果对比。结果表明,加入偏振参数的模糊逻辑算法能有效识别地物回波,对地物回波识别效果有明显改善,并有效解决了过度识别零速度降水区的问题。     

基于改进的经验模态分解和SSA联合算法的GPS坐标时间序列重构

基于ICEEMDAN算法无需先验信息即可准确分离和提取低频信号与趋势信息的特性,以及SSA具有较好的信号重构优势,提出基于ICEEMDAN和SSA的联合重构方法。该方法将弱周期信号利用ICEEMDAN方法进行提取与重构,可弥补SSA方法中当弱周期信号对应的Hankel矩阵的奇异值和噪声Hankel矩阵的奇异值接近时容易被噪声掩盖而难以提取的不足。通过模拟实验和真实站点数据验证该算法分解和重构精度,并与奇异谱分析法、小波分解法、滑动最小二乘法进行比较。实验结果表明,ICEEMDAN-SSA联合算法相对于已有方法具有更好的重构精度。     

基于等角边界距离相似性的遥感影像面状图斑匹配研究

基于形态特征的目标匹配是地图空间认知、地表位置自动感知等领域的重要方法。然而,由于不同数据源的遥感影像提取的同一个空间目标在空间尺度和局部形态上存在一定的差异,现有形态匹配算法难以获得较高的匹配精度。本文以等角边界距离编码及相似性算法为核心,提出了面向遥感影像的面状图斑匹配方法。(1)从面目标的质心以10°为间隔,顺时针方向从质心向外引36条射线,获取面目标的边缘点,形成等角边界距离编码;(2)提出了与该编码相适应的几个综合形态指标,包括自相似度、圆形度、形态复杂度和质心偏移度,并建立了面状图斑相似度算法;在此基础上,结合近邻目标的形态相似度,构建了遥感影像面状图斑匹配算法;(3)从不同数据源的在线遥感影像上提取中国西部湖泊与全球湖泊,开展面状图斑匹配实验。通过实验,发现本文的面状图斑相似度算法相对于按距离采样的相似度算法,召回率提升了13.8%;在添加近邻目标相似度约束的基础上,面状图斑匹配精度达到90%以上。通过算法适应性分析,发现该算法在一定的投影变形下仍能保持一定的匹配精度;如果空间目标及其邻域目标的总体形态和分布接近,不同空间尺度的匹配精度保持在80%以上。本文的研究为地图空...     

PSO-GM(1,1,N,p,ξ)模型在变形预测中的应用

通过对传统GM(1,1)缺陷分析和改进的基于权的PGM(1,1)建模机理描述,顾及PGM(1,1)中背景值构造时取相同的参数不能充分降低模型的预测误差,对不同的时刻引入不同的参数来改进GM(1,1)背景值序列的计算公式,将这种背景值构造方法和灰元N引入GM(1,1)建立了新的白化方程。在建立的新的白化方程基础上,用龙格-库塔法以修正的初始值计算累加值的模拟序列。针对引入的参数较多问题,采用粒子群算法寻找满足相对误差均值最优的参数,从而建立了基于粒子群优化算法和加权灰色组合的PSO-GM模型。工程实例应用表明,新模型的拟合精度高,预测效果好,相对其他两种原有模型预测精度有明显提高。     

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?????е???α???????λ???????и???????????μ???????????С????????????λ???????????????λ?????????????????????????????????????????????μ?????????????????????????????1??75???????￡??μ??λ????????????λ?????и???????????????????????10?????????0.02???????????     

概率积分法沉陷预计与参数反演优化算法及实现

任意多边形工作面沉陷预计及精确的求取概率积分法反演参数是开采沉陷研究的重点。提出了使用Delaunay三角剖分将任意多边形开采区域划分为若干小三角形,然后进行沉陷预计的方法,解决了任意多边形沉陷预计的难题;综合模矢法和遗传算法优点,提出了组合算法,提高了概率积分法参数反演精度和效率;开发了集实测数据处理、预计模型参数反演和移动变形预计模块于一体的地表移动变形数据处理软件。优化算法的提出及系统开发,可以更好的服务于"三下采煤"、矿区建筑物稳定性评估、矿区土地复垦、采煤塌陷地治理规划设计等工作。     

基于SSA-Adam-BP神经网络模型的堰塞坝稳定性预测

现有的堰塞坝稳定性预测模型多为线性模型,无法充分考虑堰塞坝稳定性与其形态特征和水域条件之间的复杂非线性关系。鉴于此,结合反向传播神经网络模型和樽海鞘优化算法,提出了一种新型的堰塞坝稳定性预测模型SSA-Adam-BP。该模型通过网格搜索法选取确定模型结构的最佳超参数组合,进而利用交叉验证和绘制ROC曲线的方式分别对采用不同优化算法的模型进行评估。使用开源数据库中的全球153例堰塞坝数据对模型的实际应用进行了说明及验证。与传统线性模型的对比表明神经网络模型预测准确率较高,具有较低的误报率。将SSA与Adam优化算法结合提高了BP模型的全局搜索能力,其平均交叉验证准确率达到了91.73%,能够使用较少的参数实现对堰塞坝稳定性快速准确的预测。SSA-Adam-BP模型对近年来典型工程的稳定性能够准确预测,具有一定的实用性和系统平台推广应用价值。     

Study of elastic-plastic damage model of cement consolidated soil with high organic content

On the basis of elastic-plastic damage model of cement consolidated soil, the authors took organic contents into reasonable damage variable evolution equation in order to seek relation between the organic contents and parameters in the equation, and established the elastic-plastic damage model of cement consolidated soil considering organic contents. The results show that the parameters change correspondingly with difference of the organic contents. The higher the organic contents are, the less the valves of the parameters such as elastic modulus (E), material parameters (K, n) and damage evolution parameter (ε) become, but the larger strain damage threshold value (εd) of the sample is. Meanwhile, the calculation results obtained from established model are compared with the test data in the condition of common indoors test, which is testified with reliability.     

利用最小二乘支持向量机的短临降雨预测模型构建

针对传统降雨预测理论错报率高及算法拟合精度低等缺陷,将与降雨过程相关的多种气象参数(温度、相对湿度、露点温度、气压等)及时间参数(年积日和天积时)引入短临降雨预测模型的构建.将新加坡2个GNSS和气象并址的测站(NTUS、SNUS)2010?2012年的气象数据及降雨数据作为样本,研究气象参数与降雨数据的时变特征,结果...     

TM热波段图像的地表温度反演算法与实验分析

目前利用LandsatTM热波段数据反演地表温度有3种算法:辐射传导方程法、单窗算法和单通道算法。辐射传导方程法由于计算过程复杂且需要实时大气剖面数据,因而实际应用较为困难。单窗算法和单通道算法对Landsat热波段反演地表温度能获得较高精度。单窗算法所需的大气参数包括近地表气温和大气水分含量,单通道算法所需的大气参数仅为大气水分含量。地表辐射率为这两种算法共有的关键参数。本文以福建省福州市为研究区,使用1989年6月15日LandsatTM数据,利用单窗算法和单通道算法对研究区进行地表温度反演,并将这两种算法的反演结果与研究区反演的亮度温度进行了比较,结果表明:(1)两种算法反演的结果总体趋势比较接近,但单窗算法的结果相对于单通道算法较低,二者相差约2.45℃;(2)两种算法的结果与亮度温度相比,单窗算法要高出约2.84℃,而单通道算法则要高出约5.28℃。     

基于信噪比检验的双参数岭型Kalman滤波及其在BDS星地联合定轨中的应用

将Kalman滤波病态性诊断与处理相结合,提出基于信噪比检验的双参数岭型Kalman滤波。在分析Kalman 滤波的病态性以及岭型 Kalman 滤波的缺陷后,引入信噪比统计量度量每个待估参数受病态性影响的大小,将待估参数区分为涉扰参数和非涉参数,并利用两个岭参数对两类参数进行不同强度的修正,结合广义岭估计思想给出两个岭参数的选取方法。该算法在降低状态参数估计方差的同时尽量减少岭型Kalman滤波引入的偏差。利用STK仿真5颗GEO卫星+24颗MEO卫星+3颗IGSO卫星的北斗卫星星座并进行分布式自主定轨,结果表明提出的新算法能够有效改善病态性对Kalman滤波的不良影响,且相对于岭型Kalman滤波具有更高的定轨精度。     

沉降观测AR模型的不确定性平差算法

在沉降观测过程中,获取的数据经常存在着不确定性,影响参数估计的可靠性和沉降预测的准确性。把不确定度作为参数融入AR模型,建立基于AR模型的不确定性平差模型。依据残差中不确定性传播规律,利用min-max估计准则,使得残差中的最大不确定度达到最小,从一个新的角度探讨不确定性观测数据的处理方法,得到基于不确定性算法的AR模型,扩展了现有误差理论。通过沉降实例进一步验证算法的优越性。