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。     

弹性梁损伤识别模态应变能法

针对飞行器弹性梁结构的振动疲劳损伤问题,基于单元模态应变能的变化提出了一种新的结构损伤识别方法。该方法以结构损伤会导致其模态性能变化为原则,通过对比结构单元损伤前后模态应变能的变化率来构建损伤指标。在构建损伤指标之前,假定弹性梁的刚度是由单元刚度组成的,在定义单元刚度灵敏度公式的基础上,建立了弹性梁损伤前后模态应变能变化的联系。最后采用数值计算和实验测试方法,得到了弹性梁损伤状态的识别结果,验证了该方法对损伤识别的正确性和有效性。     

基于应变模态的模态应变能损伤识别方法

传统模态应变能计算需要完备模态振型信息,而模态振型信息中存在转角自由度难以准确获取的问题,为解决该问题,开展基于应变模态的模态应变能损伤识别研究,实现了结构损伤的定量识别。首先,通过基于应变与位移之间的联系,推导出应变模态与位移模态之间的转换矩阵;其次,利用应变模态代替位移模态计算单元模态应变能,建立基于灵敏度分析的损伤识别方程组;最后,根据奇异值截断法求解该方程组识别结构损伤。以一两端固支梁结构为对象,开展数值仿真和实验研究。结果表明,该方法可以有效识别出结构的损伤位置和损伤程度,相对于基于振型扩充的模态应变能损伤识别方法,具有更好精度和抗噪性能。     

模态应变能在复合材料机翼结构损伤检测中的应用

以复合材料机翼结构为研究对象，采用单元模态应变能改变率作为结构损伤标识量，对损伤结构进行损伤识别仿真。应用NASTRAN有限元程序对模型进行模态分析，模态分析的结果表明，单元模态应变能改变率对不同位置和不同程度损伤都具有较强的敏感性。应用神经网络成功识别了损伤的位置和程度，指出其可行性。     

基于小波分析的简支梁桥损伤识别

提出了基于小波子带信号能量曲率变化的损伤识别方法。分别对完好和损伤状态下结构的振动响应进行二进离散小波变换,通过信号子带分解与重构将响应分解到不同频带,使叠加的模态响应分离。定义了信号相对能量曲率差损伤指标,利用该指标对结构的损伤进行识别定位。应用此方法对一简支梁桥进行损伤数值分析,结果表明:二进离散小波变换可以对结构振动响应中叠加的多阶模态信息进行有效分离;信号相对能量曲率差指标可以对损伤进行有效识别,且不受激励位置及荷载大小影响。最后通过模型实验验证了该方法的正确性及可行性。     

连续小波变换在梁结构损伤诊断中的应用研究

为了检测出梁中的裂缝或因刚度降低引起的损伤,对有损伤简支梁的振型曲线进行连续小波变换．从小波系数出现模极大值有效地识别损伤的存在以及裂缝位置和刚度下降段的位置。基本振型是用小波变换识别裂缝的最佳振型．用损伤位置处振幅较大的振型曲线来识别最清楚,对有噪声影响的振型曲线同样可以用本文方法进行识别。通过分析和计算获得满意结果．在梁结构损伤诊断中具有较高的应用价值。     

基于改进粒子群算法的应变传感器优化布置

在结构健康监测和损伤识别研究中,为了应用有限的试验设备资源获取尽可能多的有效测试信息,快速有效地解决应变传感器的优化配置问题,提出了一种基于克隆选择和离散粒子群混合算法优化新型适应度函数的应变传感器优化布置方法,并将该方法应用到拉西瓦拱坝上。结果表明,基于改进克隆选择和离散粒子群混合算法具有更强的全局寻优能力,且提出的应变类适应度函数在保证应变模态正交性和模态应变能方面更有优势。该方法能很好地识别拱坝的应变振型,可在各类结构的模态测试和损伤识别研究中进行推广。     

梁结构碰撞位置的定量诊断方法

针对机械设备中广泛存在的各种梁结构常因振动等原因与外界发生碰撞而不能及时准确检测出碰撞位置的问题,提出了一种梁结构碰撞故障位置的定量诊断方法。基于谐波平衡理论推导出碰撞梁响应信号中高次谐波成分之间的对比关系,根据无故障系统和故障系统信号的响应之差,提取某一高阶谐波分量,结合梁结构系统的有限元模型来诊断碰撞故障的具体位置。对悬臂梁和复杂梁结构的数值仿真和实验结果显示,该诊断方法能够准确判断梁结构的碰撞故障位置。通过改变未知参数的实验表明,该方法具有较好的稳健性,不需要大量的先验数据,仅用两个测点的响应数据就可以诊断出故障的准确位置,简单又便于实现在线诊断。     

On-line fan blade damage detection using neural networks

This paper presents a methodology for monitoring the on-line condition of axial-flow fan blades with the use of neural networks. In developing this methodology, the first stage was to utilise neural networks trained on features extracted from on-line blade vibration signals measured on an experimental test structure. Results from a stationary experimental modal analysis of the structure were used for identifying global blade mode shapes and their corresponding frequencies. These in turn were used to assist in identifying vibration-related features suitable for neural network training. The features were extracted from on-line blade vibration and strain signals which were measured using a number of sensors.The second stage in the development of the methodology entails utilising neural networks trained on numerical Frequency Response Function (FRF) features obtained from a Finite Element Model (FEM) of the test structure. Frequency domain features obtained from on-line experimental measurements were used to normalise the numerical FRF features prior to neural network training. Following training, the networks were tested using experimental frequency domain features. This approach makes it unnecessary to damage the structure in order to train the neural networks.The paper shows that it is possible to classify damage for several fan blades by using neural networks with on-line vibration measurements from sensors not necessarily installed on the damaged blades themselves. The significance of this is that it proves the possibility to perform on-line fan blade damage classification using less than one sensor per blade. Even more significant is the demonstration that an on-line damage detection system for a fan can be developed without having to damage the actual structure.     

基于频率变化率的结构刚度非均匀退化识别

针对不利环境作用、损伤等易造成结构局部损伤且刚度退化程度不均匀的问题,以受弯梁为研究对象,从构件动力特性入手,综合考虑损伤前后的模态挠度曲率和固有频率变化,提出了基于频率变化率的刚度非均匀退化识别方法。首先,在柔度矩阵的基础上推导模态挠度曲率,通过损伤前后模态挠度曲率的改变量识别损伤位置参数,判定损伤区域;其次,对损伤区域进行节段划分,从欧拉-伯努利梁的动力方程出发建立损伤程度、损伤区域位置参数与固有频率之间的矩阵函数,实现直接利用频率值变化评估构件不同区域损伤程度。研究结果表明,该方法能很好地识别结构局部损伤位置和损伤程度,尤其是对于结构局部刚度不均匀退化的评估具有明显的优势。     

基于模态分析的钢结构损伤识别方法研究

对钢结构损伤的准确识别是确保钢结构安全可靠的重要保证。本文提出了一种利用动力试验检测钢结构破损的方法。通过建立钢梁的有限元模型计算正常情况下梁的动力特性,利用现场采集试验数据、模态分析技术获得损坏梁的自振特性。将无损模型分段计算出自振特性,利用特征比的比较确定损伤位置。经实例分析证明该方法准确、简便、适用,提高了钢结构损伤识别的效率和精度。     

Proper orthogonal decomposition based algorithm for detecting damage location and severity in composite beams

A damage detection algorithm based on the proper orthogonal decomposition technique that can be used for structural health monitoring is presented in this study. The proper orthogonal modes are employed as dynamical invariants to filter out the influence of operational/environmental variation in the dynamic response of the structure. Finite element models of a healthy and damaged carbon/epoxy composite beam are used to generate vibration data. Varying levels of stiffness reduction for the elements in the damaged zone of the structure are used to simulate impact damage. Three damage locations (center of beam, fixed end, and free end) with three damage cases for each location are investigated. Different random force inputs are used to introduce variations in the loading conditions of the beam. The results show that the developed algorithm is capable of detecting both location and severity of damage even under changing loading conditions, with a high level of confidence.     

STRUCTURAL HEALTH MONITORING OF AN AIRCRAFT JOINT

A major concern with ageing aircraft is the deterioration of structural components in the form of fatigue cracks at fastener holes, loose rivets and debonding of joints. These faults in conjunction with corrosion can lead to multiple-site damage and pose a hazard to flight. Developing a simple vibration-based method of damage detection for monitoring ageing structures is considered in this paper. The method is intended to detect damage during operation of the vehicle before the damage can propagate and cause catastrophic failure of aircraft components. It is typical that only a limited number of sensors could be used on the structure and damage can occur anywhere on the surface or inside the structure. The research performed was to investigate use of the chirp vibration responses of an aircraft wing tip to detect, locate and approximately quantify damage. The technique uses four piezoelectric patches alternatively as actuators and sensors to send and receive vibration diagnostic signals.Loosening of selected screws simulated damage to the wing tip. The results obtained from the testing led to the concept of a sensor tape to detect damage at joints in an aircraft structure.     

Crack detection in simply supported beams without baseline modal parameters by stationary wavelet transform

There are significant changes in the vibration responses of cracked structures when the crack depth is significant in comparison to the depth of the structure. This fact enables the identification of cracks in structures from their vibration response data. However when the crack is relatively small, it is difficult to identify the presence of the crack by a mere observation of the vibration response data. A new approach for crack detection in beam-like structures is presented and applied to cracked simply supported beams in this paper. The approach is based on finding the difference between two sets of detail coefficients obtained by the use of the stationary wavelet transform (SWT) of two sets of mode shape data of the beam-like structure. These two sets of mode shape data, which constitute two new signal series, are obtained and reconstructed from the modal displacement data of a cracked simply supported beam. They represent the left half and the modified right half of the modal data of the simply supported beam. SWT is a redundant transform that doubles the number of input samples at each iteration. It provides a more accurate estimate of the variances at each scale and facilitates the identification of salient features in a signal, especially for recognising noise or signal rupture. It is well known that the mode shape of a beam containing a small crack is apparently a single smooth curve like that of an uncracked beam. However, the mode shape of the cracked beam actually exhibits a local peak or discontinuity in the region of damage. Therefore, the mode shape ‘signal’ of a cracked beam can be approximately considered as that of the uncracked beam contaminated by ‘noise’, which consists of response noise and the additional response due to the crack. Thus, the modal data can be decomposed by SWT into a smooth curve, called the approximation coefficient, and a detail coefficient. The difference of the detail coefficients of the two new signal series includes crack information that is useful for damage detection. The modal responses of the damaged simply supported beams used are computed using the finite element method. For real cases, mode shape data are affected by experimental noise. Therefore, mode shape data with a normally distributed random noise are also studied. The results show that the proposed method has great potential in crack detection of beam-like structures as it does not require the modal parameters of an uncracked beam as a baseline for crack detection. The effects of crack size, depth and location, and the effects of sampling interval are examined.     

利用试验模态分析及神经网络技术对结构损伤检测的探讨

选用一工程上常用的截面开口槽形梁,通过试验模态分析对结构损伤引起的模态频率移动进行了分析,从试验角度对四种损伤检测方法进行了有效性比较。并尝试将神经网络与试验模态分析技术相结合应用到结构损伤定量检测的研究中,不仅验证了其可行性,而且指出应变指标对结构损伤更敏感。     

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。     

桥梁结构损伤的振动模态检测

首先介绍了桥梁结构损伤检测的振动模态方法的原理及其４ 个要素。总结了用模态参数（固有频率、阻尼比及位移振型）进行损伤检测评估的困难,进而阐述应变模态测量的原理及其用于桥梁结构损伤检测时,相对于位移模态的优势。由于应变模态方法应用于实桥检测受到传统应变测量手段的制约,本文最后提出了一种通过相邻点振动位移的测量,推估桥梁在动载作用下的应力状态,进行实桥损伤检测的途径,并推荐一种新型低频高灵敏度传感器作为位移测量用传感器。     

Localization and quantification of damage in beam-like structures using sensitivities of principal component analysis results

Principal component analysis (PCA) is known as an efficient method for dynamic system identification and diagnosis. This paper addresses a damage diagnosis method based on sensitivities of PCA in the frequency domain for linear-form structures. The aim is not only to detect the presence of damage, but also to localize and to evaluate it. The Frequency response functions measured at different locations on the beam are considered as data for the PCA process. Sensitivities of principal components obtained from PCA to beam parameters are computed and inspected according to the location of sensors; their variation from the healthy state to the damaged state indicates damage locations. The damage can be evaluated next providing that a structural model is available; this evaluation is based on a model updating procedure. It is worth noting that the diagnosis process does not require a modal identification achievement. Both numerical and experimental examples are used for better illustration.     

综合模态H2范数下致动器/传感器的优化配置

致动器/传感器的优化配置问题是智能柔性结构振动主动控制中的关键技术问题,基于模态空间H_2范数研究了智能柔性梁系统中压电致动器/传感器的优化配置问题。根据Rayleigh-Ritz理论建立了系统的动力学方程并得到其状态空间表达式。提出了一种衡量系统能控/能观性并考虑模态权重的综合模态H_2范数准则,采用改进遗传算法研究系统中并置致动器/传感器的优化配置问题,得到了系统多个模态、综合模态H_2范数最优的致动器/传感器布局结果。实验结果表明,利用优化结果进行致动器/传感器的布局,系统单个模态和综合模态均具有较好的检测和控制效果,被控模态具有较好的能控/能观性,所提出的优化准则和优化方法是可行的。