【作者】 李睿；

【导师】 于德介；

【作者基本信息】 湖南大学 ， 机械制造及其自动化， 2007， 博士

【摘要】 在航空航天、机械和土木工程等领域中,如果能对重要结构实施有效的健康监测,实时评估结构状况,将能有效保障结构的安全、可靠运行,避免重大事故的发生。结构损伤诊断技术是结构健康监测的主要支撑技术,其研究具有重要的理论意义与实用价值。基于振动测试的结构损伤诊断方法是目前结构损伤诊断领域研究应用最广泛的方法之一,它具有不影响结构正常工作,可实现长期或在线监测,操作简单方便等特点。按照损伤识别所需特征量是否依赖结构数值模型(一般是有限元模型),基于振动测试的结构损伤诊断方法可分为基于模型的方法和不基于模型的方法两大类。基于模型的结构损伤诊断方法需要建立结构准确的数值模型,难以应用于大型复杂结构。不基于模型的结构损伤诊断方法直接从振动响应中提取特征识别损伤,它不需要建立结构数值模型,便于工程应用。特征提取和模式识别是不基于模型的结构损伤诊断方法的两个主要环节。为了进一步改进和完善不基于模型的结构损伤诊断方法,本文围绕特征提取和模式识别这两个主要环节,以湖南省交通研究院项目(编号200223)“大型桥梁状态在线监测技术研究及监测系统开发”为背景,对基于时域响应的结构损伤敏感特征提取和结构异常检测进行了深入系统地研究,主要研究工作包括:1、针对频域特征存在的对结构早期损伤不敏感的局限性,在相空间重构和奇异值分解的基础上,从结构振动的时域响应序列中提取奇异谱作为损伤特征,提出了一种以奇异谱互熵变化来诊断结构是否发生损伤的方法。该方法从时域直接提取特征,可以避免时频转化时的信息丢失,能够提高对损伤的敏感度;能通过奇异谱分析对响应信号进行降噪处理,提高方法的抗噪声干扰能力。以Benchmark结构为对象,数值仿真验证了方法的有效性。2、针对传统的基于概率统计的异常检测方法需预先估计数据的概率分布,不适于实际应用的局限性,提出了基于人工免疫机制的结构损伤检测方法。改进了肯定选择算法,数值仿真研究了否定选择算法和肯定选择算法对损伤识别的检测率和虚警率,讨论了本体匹配半径和检测子数对检测结果的影响,分析了高斯激励和非高斯激励下损伤检测的有效性。基于人工免疫机制的结构损伤检测方法不需要先验知识,可以通过调整本体匹配半径实现在低虚警率条件下达到较高检测率的目的,无论是否定选择算法还是肯定选择算法,其检测率均明显高于概率统计方法。3、针对一般异常检测方法需要建立学习模型的局限性,提出了基于密度的结构损伤检测方法。基于密度的结构损伤检测方法具有无需了解结构响应数据的分布知识,无需建立学习模型,并且支持数据的动态更新等优点。给出了环境激励下基于密度检测结构损伤的方法及步骤,定义了两种判断损伤程度的指标--相对局部异常因子及相对局部稀疏系数,提出利用损伤前后相对局部异常因子变化率和相对局部稀疏系数变化率来诊断损伤位置。以Benchmark结构损伤诊断及某大桥船碰桥墩监测数据分析为例,验证了所提方法的有效性,并讨论了相关参数对损伤及其位置识别的影响。4、针对环境激励存在激励能量小、噪声影响大的缺点,提出了移动载荷作用下基于能量比的桥梁损伤检测方法。该方法用损伤前后各测点的能量比构成能量比曲线,根据曲线的局部极大值来检测损伤。以简支梁为对象,将匀速行驶的汽车简化为移动常量力,仿真验证了所提方法对单点损伤、多点损伤识别的有效性,分析了测量噪声、常量力、速度、窗长、测点位置及测点数目对检测结果的影响。5、根据数字图像处理中边缘点的检测原理,将桥梁损伤位置看作是边缘点,提出了移动载荷作用下基于边缘算子的桥梁损伤检测方法。该方法从测点的振动响应提取小波包能量,以损伤前后小波包能量比作为特征,应用一维边缘算子计算边缘幅度,由边缘幅度曲线的局部极大值判断损伤位置。对单点损伤、多点损伤及损伤程度检测进行了研究,数值仿真验证了该方法的有效性。更多还原

【Abstract】 Monitoring the condition and performance of vital structures is an essential issue in mechanical, civil and aerospace structures to ensure the service quality of structures and avoid grave accident. The structural damage detection technique is the fundamental technique of the structural health monitoring. Therefore, the researches on it are of great theoretical significance and practical value.By far, vibration-based damage detection technique has been the most widely used damage detection technique. The attractiveness of the technique is that it has no requirement that the structure under performance be taken out of service for inspection and an automated continuous monitoring system can be developed because of its simplicity, minimum interaction with users. According to whether or not the feature extraction demands the structural mathematic model (generally FEM model), the vibration-based technique can be divided into two categories, namely the model-dependent method and the model-free method. The model-dependent method has to establish the accurate mathematical model of the structure, which would be quite challenging in case of large-scale structures. Instead, the model-free method has no requirement to establish the mathematical model of the structure. It solely relies on structural responses, which is easy available for practical implementation. In the whole process of the model-free method, feature extraction portion and classification portion are key parts. For the improvement and perfection of the model-free method, with the support of the project (serial number 20023) entitled‘Research and Development of Health Monitoring System for Long Span Bridge’which is provided by The Department of Communication of Hunan Province, further studies on these two portions are addressed in this dissertation. The main research work of the dissertation includes two aspects: one is the damage-sensitive features extraction from time domain response, the other is the structural abnormity detection. The main work is as follows:1. The features extracted from frequency domain mostly have low sensitive to early damage. Aiming at this limitation, the singular spectrum extracted from the structural time domain response based on phase state reconstruction combined with singular value decomposition is proposed as the damage feature. In this method, the change of singular spectrum cross-entropy is introduced to indicate the structural damage. The loss of information caused by the signal transfer from time domain into frequency domain can be overcome, which could retain sensitivity of the chosen features. Applying the singular value analysis can effectively eliminate noise. For demonstration, a numerical study on the ASCE benchmark model is performed. The results show that the health condition of the structure can be effectively monitored by the proposed method.2. The outlier detection method based on statistics has to require a prior knowledge of probability distribution of the structural response. In most case, this knowledge is difficult to acquire, which would induce the method unsuitable for practical application. To solve this problem, the outlier detection method motivated by artificial immune system is put forward. First the improvement on the positive selection algorithm is implemented. Next the numerical studies on the efficiency of damage detection employing the negative and positive selection algorithms are carried out. Then the influences on the detection resulting from self match radius and detector number are discussed. Last the validity of the method for damage detection under Gauss excitation and non-Gauss excitation is investigated. The research results conclude that the proposed method hasn’t to be provided with the prior knowledge. No matter which method, negative selection algorithm or positive selection algorithm, its detection rate is superior to that of the method based on statistics. In addition, it could acquire a good detect rate at low warning rate by adjusting the self match radius.3. Most outlier detection methods have to set up the learning model. Due to this problem, the outlier detection method based on density is adopted. Some appealing features of this method are: (1) the learning model is not in demand; (2) the dynamic updating of the data can be sustained. The method and the steps for structural damage detection under ambient excitation are described. Two indicators– the relative local outlier factor and the relative local sparsity coefficient for quantify the damage are defined. The ratios of the indicators pre- damage and post-damage are introduced to indicate the damage location. Numerical analysis on Benchmark model and the analysis for the monitoring data from collision between a vessel and the pier of a Bridge demonstrate that the method can reliably detect the structural abnormity. The effects of the relative parameters on the detection are studied.4. Structural ambient vibration response has the disadvantages of low energy and easy be affected by noise. In view of the fact, a method using energy ratio for identify damage in a beam subjected to moving load is developed. The idea of the method is to check the local extreme maximum value of the energy ratios curve, which is plotted with the energy ratio of the pre-damage to that of the post-damage structure at every measurement position. In the numerical simulation, the vehicle is modeled as an unchangeable moving force at invariable velocity, and the bridge is simplified as a continuous Euler-Bernoulli beam simply-supported at both ends. The feasibility of the method to detect single and multiple damages is validated. The effects of the noise, force, speed, window length, measurement position and measurement number are studied. 5. Based on the principle of edge point detection in digital image, the damaged position in a structure can be regarded as an edge point in digital image and a method using an edge operator to assess damage subjected to moving load is presented. The squares of the wavelet packet energy from the measurement positions are obtained. The ratio of the square pre- damage to the square post-damage is served as the indicator. On this condition, the edge amplitude curve is plotted with the edge operators. Then the damaged location can be judged according to the local extreme maximum value of the curve. The effectiveness of the method to detect single and multiple damages and quantify the damage is numerically validated.更多还原