基于振动传递率函数和奇异值熵的结构损伤检测方法

为了直接通过结构振动响应提取损伤特征,对激励未知情况下的结构损伤进行检测,提出了基于振动传递率函数和奇异值熵的损伤检测方法。该方法首先通过振动响应获得振动传递率函数,然后对振动传递率函数序列进行相空间重构,求取其奇异值熵,通过奇异值熵的大小来识别损伤模式。实验结果表明,该方法能有效地识别结构的损伤模式。     

基于多尺度振动传递率函数和灰度矩相对熵的损伤识别方法

为了从结构振动响应中直接提取损伤敏感参数,对激励未知情况下的结构损伤模式进行识别,提出了基于多尺度振动传递率函数和灰度矩相对熵的损伤识别方法。对振动响应进行小波分解,通过计算不同尺度下的互相关函数,求解得到多尺度振动传递率函数。为了通过多尺度振动传递率函数提取损伤指标,计算了不同尺度范围内的多尺度振动传递率函数的灰度矩向量,该指标不受工况变化的影响。通过计算测试数据与样本数据灰度矩向量之间的相对熵,对结构的损伤模式进行了识别。实验结果表明,该方法能有效识别结构的损伤模式,适用于激励未知且工况变化情况下的损伤识别。     

运用统计小波的光纤光栅结构健康监测技术

针对振动型结构健康监测方法的特点,搭建了基于非平衡M-Z干涉仪和相位载波解调技术的光纤光栅损伤识别系统。运用小波包分解振动信号,建立了基于小波包节点能量相对变化率之和的结构损伤识别指标;介绍了统计过程控制原理,推导了使用均值-极差控制图分析损伤识别指标,识别结构连续损伤的过程。实验测试了铝制简支梁结构处于健康状态和3种损伤状态下的各40次振动信号。信号时域图显示各状态振动信号持续时间均约为0.05ms,幅值基本相同。依据结构健康状态下的统计过程控制限(12.85,41.35)进行了均值-极差控制图损伤识别分析,结果表明,搭建的损伤识别系统能连续地对结构进行健康监测。     

基于SPC与小波变换的光纤光栅型损伤识别

为了研究光纤光栅传感技术在振动型损伤识别技术中的优势,建立了基于非平衡M-Z干涉仪和PGC相位解调技术的光纤光栅结构损伤识别系统;构建了基于小波包节点能量相对变化率之和的结构损伤识别指标;介绍了统计过程控制原理,推导了均值-极差控制图进行结构连续损伤识别的过程;测试了结构处于健康状态和6种损伤状态下的振动信号并分析了损伤识别.试验结果表明,构建的损伤识别系统能判断损伤的存在,并且能对损伤的位置和损伤程度进行有效识别.     

基于振动分析的机械结构损伤识别方法综述

机械结构的损伤识别是机械结构健康监测的核心,也是一项具有挑战性的研究课题。基于振动的机械结构损伤识别方法在国内外已经受到广泛的关注和重视,成为世界范围的热点问题。首先叙述了机械结构损伤识别的涵义与一般识别步骤,然后综述了目前较为主流的机械结构损伤识别方法的基本理论、各自的识别优势以及尚待解决的不足之处,最后,简述了目前基于振动分析的机械结构损伤识别方法中尚待解决的一些关键问题。     

基于VMD-HHT边际谱的水工结构损伤诊断

为了准确识别水工结构的损伤,提出一种变分模态分解(variational mode decomposition,简称VMD)和Hilbert-Huang变换(Hilbert-Huang transform,简称HHT)边际谱相结合的水工结构损伤诊断方法。首先,采用联合的小波阈值和经验模态分解(empirical mode decomposition,简称EMD)降噪方法对原始信号进行降噪,减小环境噪声对结构损伤特征信息的干扰;其次,运用方差贡献率数据融合算法对降噪后各测点信号进行动态融合,提取结构完整的振动特性信息;然后,采用VMD方法将动态融合信号分解为一系列固态模量(intrinsic mode function,简称IMF),对各IMF分量进行Hilbert变换,求出融合信号的边际谱;最后,在VMD边际谱的基础上提取一种新的损伤特征向量-损伤灵敏指数,将其与马氏距离相结合对水工结构的损伤类型进行分类,并将该方法应用于悬臂梁模型试验。结果表明:该方法能够有效提取水工结构的损伤特性,准确识别水工结构的损伤和运行状态,为水工结构的安全运行提供了基础。     

光纤光栅型智能结构损伤识别的小波包分析

基于结构振动监测技术,采用小波包分析方法对采集的结构振动信号进行了小波分解。介绍了小波包分解技术,提出将小波包能量谱作为损伤指标用以表征结构的损伤状态。在相同损伤位置不同损伤程度、不同损伤位置相同损伤程度的两种情况下对试件进行了六种不同损伤工况的振动信号分析。数据分析结果表明,结构的损伤将导致结构振动信号小波包分解中特定阶数的能量增加,而所提出的损伤指标对结构的损伤程度、损伤距离及损伤位置与光栅传感器之间的角度均敏感,实验中能够识别12 g重物且距离光纤光栅传感器30 cm的损伤,表明对智能结构的在线损伤识别是可行的。     

基于贝叶斯方法的统计模型修正和结构概率损伤识别

提出了一个用马尔科夫链抽样的贝叶斯模型修正和损伤识别方法,用于对梁结构的损伤识别.首先建立了基于结构振动频率测量,确定其振型的目标函数,然后采用延缓拒绝自适应的马尔科夫链蒙特卡洛方法进行随机抽样,得到在完好状态和损伤状态下结构参数的后验概率,通过比较这两种状态下识别参数的概率密度函数,最终得到结构损伤的概率和损伤程度....     

基于广义柔度和附加质量的结构损伤识别研究

通过在结构的某些部位附加已知的集中质量块,测量附加质量后结构振动的低阶模态,把这些新的模态参数及原结构的模态参数联合起来,通过广义柔度灵敏度来建立损伤识别方程求解损伤参数。以一个桁架结构为例对所提的方法进行验证。研究结果表明,仅需要测量原结构和附加质量后结构的第一阶模态信息就可以比较准确地识别出结构的损伤,说明了所提方法的可行性,为解决大型结构的损伤识别问题提供了一条可供参考的途径。     

基于曲率模态的复合材料层合板分层损伤检测方法研究

基于振动信号应用曲率模态方法对复合材料层合板分层损伤进行损伤检测。首先由数值模拟所得不同状态下损伤以及未损伤层合板振动特性参数计算其振型曲率和均布载荷曲面值曲率;然后基于损伤与未损伤层合板曲率值的差分值建立损伤识别指标进行分层损伤检测。最后,为解决差分算法对结构未损伤模型参数的依赖性问题,引用曲面光滑算法和曲率模态变化率法直接对损伤结构进行损伤提取。结果表明,运用曲率参数差分法可以很好地识别各类损伤,且对于多层层合板来说损伤所处层的位置会影响损伤指标值的大小。曲面光滑算法和曲率模态变化率法均可不依赖于未损伤结构参数识别各类损伤,且曲面模态变化率法的检测效果优于曲面光滑算法。     

基于CMSE理论和遗传算法的结构损伤检测方法研究

提供了一种基于交叉模态应变能(CMSE)的结构损伤位置和严重程度的有效检测方法。传统的模态应变能方法必须比较损伤结构和完好结构的同一阶模态信息,并且要求解析的和测量的模态在尺度上一致或者标准化,而CMSE方法没有这样的限制。在损伤位置的检测上引入了遗传算法,有利于大型复杂结构的损伤位置检测。算例采用了复合材料机翼盒段模型,结果表明,CMSE方法与遗传算法相结合是检测结构损伤的有效方法之一。     

Feasibility study of structural damage detection using NARMAX modelling and Nonlinear Output Frequency Response Function based analysis

Nonlinear Output Frequency Response Functions (NOFRFs) are a series of one-dimensional functions of frequency recently proposed by the authors to facilitate the analysis of nonlinear systems in the frequency domain. The present study is concerned with a feasibility study of the application of the well-known Nonlinear Auto-Regressive Moving Average with eXogenous Inputs (NARMAX) modelling method and the NOFRFs-based analyses to the detection of damage in engineering structures. The new technique includes three steps. First, a NARX model is established by applying the NARMAX modelling method to input and output data collected from a test on an inspected structure. Then, the NOFRFs and an associated index for the inspected structure are determined from the established NARX model. Finally, structural damage detection is conducted by comparing the values of the NOFRF index of the inspected structure with the values of the index for a damage-free structure. An experimental application to the detection of damage in aluminium plates demonstrates the potential and effectiveness of the new damage detection technique.     

大型复杂结构损伤识别两步法研究

基于模型修正技术及模态柔度曲率差方法提出了一种解决大型复杂结构损伤识别问题的两步法。首先运用基于模型修正的损伤识别方法进行模糊识别,通过建立带约束边界非线性最小二乘目标函数,极小化结构实测模态与解析模态之间的误差,将损伤识别问题转化为优化问题,并采用信赖域方法求解该优化问题,识别出损伤所属单元组。然后运用模态柔度曲率差方法,对损伤进行精确定位。对某导弹发射台骨架的数值仿真及试验研究结果表明,该损伤识别两步法识别效果较为理想,为解决大型复杂结构的损伤识别问题提供了新思路。     

结构损伤的分形神经网络检测方法

利用神经网络具有强大的非线性并行处理能力以及分形几何方法不依赖于系统的数学模型的特点,将分形维数与神经网络相结合,建立了结构损伤的分形神经网络检测方法。研究结果表明,结构不同状态下的振动信号的分形维数有明显的不同,可以将分形维数作为结构损伤检测的特征量,并用神经网络将结构的不同状态模式识别出来。     

频响函数在结构损伤检测中应用的试验研究

结构动力测试中,频响函数可直接测得,不仅避免了在模态提取时的误差,而且在相同频段上可提供更多结构损伤信息,因此可方便地应用于结构损伤的检测中。对于结构基础的随机激励,试验结果表明频响函数法可有效地检测结构的损伤。同时说明传感器布置在结构的不同位置都可检测结构的损伤。并且随机振动的大小不影响结构损伤检测的效果。为频响函数法在现场实测结构的损伤提供了依据。     

一种多目标传感器优化布置方法及其应用

为解决复杂结构损伤识别中的传感器优化布置问题,以某发射台为研究对象,提出了一种多目标传感器优化布置方法(multi-objective optimum sensor placement,简称MO-OSP)。从结构运动方程出发,推导了同时具有各自由度模态独立性信息、损伤灵敏度信息以及运动能量信息的综合信息矩阵。根据信息熵原理,以协调灵敏度矩阵条件数最小和信息矩阵最大为目标,构造了能够兼顾算法敏感性和鲁棒性的目标函数,进而实现测点优选。采用多个评价准则和损伤识别实例,将所提方法与已有的3种典型传感器优化布置方法进行了对比分析。结果表明,提出的MO-OSP方法能充分满足模态线性独立和损伤敏感性,还具有较强的抗噪声性能,是解决复杂结构损伤识别中传感器优化布置问题的有效方法。     

基于灵敏度分析的机械系统损伤识别方法

提出了一种基于振动灵敏度分析的机械系统损伤识别方法。该方法同时考虑了固有频率灵敏度和固有模态灵敏度 ,既可用于损伤定位 ,也能用于确定损伤大小 ,且对单个损伤和多个损伤情况都适用。为了提高识别的精度 ,考虑了二阶灵敏度。针对工程实际的需要 ,分析了不完备模态和模态测量误差对该方法识别精度的影响。算例表明 :本方法合理可靠 ,具有足够的精度。     

Improvement of damage detection methods based on experimental modal parameters

The objective of this paper is to introduce a new method for structural damage detection based on experimentally obtained modal parameters. The new method is suitable for detection of fatigue damage occurring in an aluminium cantilever beam. The damage has been practically realised as saw cuts of different sizes and at different locations. The first step of analysis included an attempt of damage identification with the most often used damage indicators based on measured modal parameters. For that purpose special signal processing technique has been proposed improving the effectiveness of indicators tested. However the results obtained have not been satisfactory. That was the motivation for defining new damage indicators (frequency change based damage indicator, Hybrid Damage Detection method), utilising the change of natural frequencies and any mode shape (measured or modelled) as the measurement of frequencies is much less time consuming in comparison to total mode shape measurement. It has been shown that the proposed technique is suitable for damage localisation in beam-like structures.     

An experimental study on damage detection of structures using a timber beam

Using vibration methods for the damage detection and structural health monitoring in bridge structures is rapidly developing. However, very little work has so far been reported on timber bridges. This paper intends to address such shortcomings by experimental investigation on a timber beam using a vibration based method to detect damage. A promising damage detection algorithm based on modal strain energy was adopted and modified to locate/evaluate damage. A laboratory investigation was conducted on a timber beam inflicted with various damage scenarios using modal tests. The modal parameters obtained from the undamaged and damaged state of the test beam were used in the computation of damage index, were then applied using a damage detection algorithm utilising modal strain energy and a statistical approach to detect location of damage. A mode shape reconstruction technique was used to enhance the capability of the damage detection algorithm with limited number of sensors. The test results and analysis show that location of damage can be accurately identified with limited sensors. The modified method is less dependent on the number of modes selected and can detect damage with a higher degree of confidence.     

Moving-window extended Kalman filter for structural damage detection with unknown process and measurement noises

The extended Kalman filter (EKF), as a popular tool for optimally estimating system state from noisy measurement, has been used successfully in various areas over the past several decades. However, classical EKF has several limitations when applied to structural system identification; thus, researchers have proposed a number of variations for this method. The current study focuses on using EKF for real-time system identification and damage detection in civil structures. An improved EKF approach, called moving-window EKF (MWEKF), is proposed in this paper after a discussion on the problems associated with the application of classical EKF in time-variant systems. The proposed approach uses the moving-window technique to estimate several statistical properties. MWEKF is more robust and adaptive in structural damage detection compared with classical EKF because of the following reasons: (1) it is insensitive to the selection of the initial state vector; (2) it exhibits more accurate system parameter identification; and (3) it is immune to the inaccurate assumption of noise levels because measurement and process noise levels are estimated in this approach. The salient features of MWEKF are illustrated through numerical simulations of time-variant structural systems and an experiment on a three-story steel shear building model. Results demonstrate that MWEKF is a robust and effective tool for system identification and damage detection in civil structures.