Detection of nonlinearity effects in structural integrity monitoring methods for offshore jacket-type structures based on principal component analysis

The detection of changes in the dynamic behavior of structures is an important issue in structural safety assessment. The development of detection methods assumes greater significance in the case of offshore platforms because the inherent problems are compounded by the harsh environment. Here, we describe an instrumented physical model for the structural health monitoring of an offshore jacket-type structure and the results of tests in several different damage scenarios. In a comparative investigation of two different methods, we discuss the difficulties of implementing damage detection techniques for complex structures, such as offshore platforms. The combined algorithm of a fuzzy logic system and a model updating method are briefly discussed, and a method based on stochastic autoregressive moving average with exogenous input is adopted for the structure. The consideration of uncertainties and the effects of nonlinearity were major objectives. So, the methods were also investigated based on the test scenarios consisting of the physical model with a geometric nonlinearity. The principal component analysis method was utilized for the detection of nonlinearity in the recorded data. The results show that the developed methods are suitable for damage classification, but the quality of the acquired signals must be considered an important factor influencing successful classification. The development of these methods may be extremely useful, as such technologies could be applied for offshore platforms in service, enabling damage detection with fewer false alarms.     

Plate scanning tools to obtain travel times in traffic networks

The management of vehicle travel times has been shown to be fundamental to traffic network analysis. To collect travel time measurement, some methods focus solely on isolated links or highway segments, and where two measurement points, at the beginning and at the end of a section, are deemed sufficient to evaluate users' travel time. However, in many cases, transport studies involve networks in which the problem is more complex. This article takes advantage of the plate scanning technique to propose an algorithm that minimizes the required number of registering devices and their location in order to identify vehicles candidates to compute the travel times of a given set of routes (or subroutes). The merits of the proposed method are explained using simple examples and are illustrated by its application to the real network of Ciudad Real.     

基于灰色理论的雪山梁隧道施工过程渗漏水水源识别研究

为了解决黄龙景区雪山梁隧道开挖过程中的涌水水源识别问题,结合黄龙景区雪山梁隧道的相关施工资料,依据已有的地质资料和涌突水预测,通过水分析化学实验,采用灰色理论计算方法研究不同水系之间的关联度;而后针对单一水源和混合水源分别进行试验和分析,得出雪山梁隧道出口段内渗漏水来源最可能为大气降水,关联度较高的还有淘金沟上游水系和隧道出口处水系;最后进行比对,得出隧道渗漏水主要来源是大气降水和淘金沟上游水体的结论,并依据现场资料及实地踏勘,验证了灰色理论分析结果的可靠性。     

基于小波和粒子群算法的HEV行驶状况辨识方法研究

针对混合动力汽车(HEV)行驶状况(道路坡度和整车载荷)变化难以有效识别,导致驱动系统控制策略不能有效满足驾驶员意图问题,以混联式HEV为研究对象,提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型,采用最小二乘法确立了优化目标函数,其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理,最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上,对实车数据进行小波滤波,并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识,并对辨识结果进行小波滤波,结果表明,试验工况下整车载荷辨识的相对误差绝对平均值为2.71%,道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。     

射频识别技术在物流工程中的应用研究

简要介绍了射频识别技术的工作原理和优点,综述了射频识别技术在国内各个领域的主要应用,最后指出了当前射频识别技术所存在的主要问题。     

自行车流视频识别判断方法研究

考虑算法的实时性,交通流个体识别往往采用轮廓特征来描述。由于自行车与行人的很多轮廓特征值相近,在复杂城市交通环境中,从慢行交通流中准确地识别出自行车是目前采用视频检测交通流的难点之一。通过现场视频检测获取足够的样本外轮廓数据,分析检测目标轮廓的高、宽、高宽比、面积等典型轮廓特征,发现目标轮廓高宽比特征能较好地识别出行人、自行车等慢行交通,并给出合适的取值范围。研究表明自行车高宽比能够较好地成为视频识别的依据,为自行车视频识别提供数据。     

高速铁路声屏障在列车脉动风载荷下的强度计算

为了得到高速列车声屏障在列车经过时的受力规律,建立了声屏障的外流场模型和有限元模型。利用流固耦合技术,计算分析了声屏障的三维动态流场和结构模型,得到了列车风气动力特性和H钢立柱的受力规律。计算结果表明:列车经过时声屏障受到的压力为一个随时间变化且不均匀分布的面载荷。H钢立柱发生弯扭组合变形,弯矩扭矩各经历两次换向。每当列车经过一次,等效应力产生四个脉冲。H钢立柱受到的应力不大,可能出现疲劳破坏。     

焊接顺序对高速列车侧墙焊接残余应力的影响

按实际加工工艺,基于有限元软件SYSWELD,采用有限元热弹塑性分析方法和Fortran语言对焊接热源进行二次开发,获得了所需要的热源,并通过实验验证了热源模型的准确性.在此基础之上,通过分析高速列车CRH380B侧墙焊接残余应力分布规律和焊接顺序对于侧墙焊接残余应力的影响,得到了侧墙焊接最优方案,为企业实际生产中的降低焊接残余应力方案选择提供了依据.     

盲源分离技术在水声信号中的应用研究

通过研究α稳定分布,提出海洋噪声、舰船辐射噪声等符合低阶α稳定分布,在进行对称α稳定分布且独立的随机源信号进行盲分离处理时,若仍然采用高斯分布将不收敛。本文设计了分数低阶矩阵盲源分离算法,最后通过实验对比本文所设计算法和时延分离算法。实验结果表明:本文所设计的算法对于α稳定分布且有独立的随机信号,具有较好的收敛性,能够有效实现盲源信号分离。通过从复杂的海洋噪声中仍能较好地提取出船舱辐射噪声的实验,说明即使信噪比发生变化,源信号分离后相似系数较分离前仍然有稳定地提高。     

基于改进粒子群算法的PMSM多参数辨识

针对标准粒子群优化算法对永磁同步电机多参数辨识精度低与收敛慢的问题,设计了一种自适应自治群组粒子群优化算法进行辨识,并在Matlab/Simulink中搭建参数辨识模型.仿真结果表明:改进后的算法对永磁同步电机多参数辨识的整体精度更高,收敛速度更快.