部分输入未知条件下结构参数识别法研究

深入研究了输入信息测试不完备条件下的结构参数识别问题，从理论上论证了部分输入未知时动力复合反演问题补偿算法的实质，指出了全量补偿算法在严格意义上的适用条件，提出了更为简便的二阶段识别法，使部分输入未知条件下结构参数识别的理论进一步完善，并可为工程应用提供指导。数值算例验证了理论上的正确性。     

基于新的圆锥补偿结构的姿态算法（英文）

提出基于一种新的圆锥补偿结构的捷联惯导姿态算法。与传统的姿态算法不同,新算法中同时引入了角速率和角增量用于圆锥补偿(适用于角速率输入或角速率和角增量同时输入)。基于所提出的圆锥误差补偿结构,引入时间泰勒方法进行圆锥误差补偿优化设计,并定义了两种性能评价模型,以分别用于一般圆锥和机动环境下的姿态算法性能评估。将新的姿态算法与传统角增量输入的姿态算法通过仿真进行了对比分析,结果表明,在相同的采样频率和姿态更新周期以及相同的圆锥和机动环境的条件下,新的五子样姿态算法的性能明显优于传统角增量输入的五子样姿态算法。     

未知输入条件下的结构物理参数识别研究

研究在输入信息未知条件下识别结构物理参数的问题，根据建筑结构风荷载的作用特点，提出一类时域识别算法，用于高层建筑结构的结构物理参数识别     

基于同心压电传感器的单模态Lamb波信号提取方法

Lamb波具有传播范围远、对小损伤敏感等优点,适用于飞机机翼、壁板等大面积结构的损伤检测,然而Lamb波的频散和多模态特性使得单模态难以识别和解析信号。本文提出一种适用于板壳结构损伤检测的单模态提取方法,设计并使用一种同心压电传感器,根据所激励各模态的幅值比来分解信号并补偿时间差,从而提取出单一模态的信号。通过仿真分析和铝板实验验证,结果显示本方法能够有效提取单一A_0模态信号,并在实验中成功识别了隐藏的裂纹损伤反射回波信号,使信号相对于传统方法变得更易解释和分析。     

基于子结构的非线性参数系统动力反演方法研究

研究了输入、输出不完备情况下的非线性参数系统动力反演问题.将子结构技术与分解算法相结合,引入广义逆,无需迭代.直接求得待识别参数的极小范数最小二乘解,反演获得未知输入荷载.本文从理论上论证了该方法的收敛性和严格的适用条件,为有限测点条件下非线性参数系统的动力反演问题提供了一个较好的解决方法.与全量补偿算法相比,计算效率大大提高,具有广泛的工程实际应用前景.数值算例表明该方法具有很好的参数识别精度及荷载反演效果.     

爆破地震波中S波识别方法及其应用

S波震相初至时刻的精确识别是岩石动力学参数反演的关键环节。其拾取的精度和速度直接影响到岩石动力学参数反演的精度和效率。对S波的识别研究大多都集中在地震领域，现有方法难以有效的识别工程尺度下的爆破地震波S波的到时。本文中拟提出一种适用于工程尺度下S波的识别方法。该方法不对振动信号进行滤波处理，采用短时平均过零率、偏转角、偏振度和横向能量与总能量比值等4个识别参数对S波进行识别。结合丰宁抽水蓄能电站地质勘探洞爆破实验实测数据识别效果与数值模拟结果表明:该方法在工程尺度下的识别误差小于3%。该算法能较好地适用于工程尺度下S波初至时刻进行识别。     

结构故障诊断中的有限元反问题方法

本文以有限元方法为基础,借助于小扰动理论,将振动结构物理参数识别问题,归结为有限元反问题。文中给出了识别的模型,用以识别由疲劳裂纹等故障引起的微小刚度变化。从而可对裂纹的发生、发展进行“在线”监测。本文的方法适用于形状较复杂的一维结构,也可推广到二维、三维结构。本文对两端固支梁结构和悬臂梁结构进行了数字模拟,结果是令人满意的。     

一种机械结构结合面动力学参数识别方法

机械结构结合面刚度和阻尼的确定是对结构进行动态分析和优化设计的关键问题之一。本文提出一种结合面动力学参数识别方法。该方法通过实测结构少量几个点上的振型和传递函数确定结合面参数。它不要求预先建立结构各部件的解析模型,也不需要实测整个结构完整的动态信息,因此适用于复杂结构结合面动力学参数的识别。文章阐明了结合面参数的识别原理,并讨论了如何消除实验误差对识别结果的影响。用本文方法识别了一台钻床的结合面参数,得到了令人满意的结果。     

压电阻尼主动控制中局部应变的电路补偿及其参数识别

论文［１］提出了在压电阻尼主动控制中用压电基片来补偿局部激励拉压应变增进阻尼控制的方法。本文则进一步提出用简单的电路来等效替代压电基片及相应的电路参数识别算法，从而使补偿变得更为简便。实验证明了两者的补偿效果相同。     

压电阻尼主动控制中局部应变电路补偿及其参数识别

论文（１）提出了在压电阻尼主动控制中用压电基片来补偿局部激励拉压应变增进阻尼控制的方法。本文则进一步提出用蝗电路来等效替代压电基片及相应的电路参数识别算法，从 为得更为简便。实验证明了两者的补偿效果相同。     

部分输入未知条件下结构动力复合反演的分解算法

在充分利用部分输入已确知而部分输入未知的激励特性的基础上，提出了结构动力复合反演的分解算法，该算法从源头上消除了迭代过程中参数识别与荷载反演的相互影响，降低了问题的计算规模。对于线性参数系统，该算法不经过任何迭代计算即可一次性完成结构参数识别及荷载反演。将其与松弛法结合，可解决非线性参数系统的识别问题，与文献[4]的方法比较，其收敛速度有显著提高。     

Newton iterative identification method for an input nonlinear finite impulse response system with moving average noise using the key variables separation technique

This paper studies parameter identification problems for input nonlinear finite impulse response systems with moving average noise (i.e., input nonlinear finite impulse response moving average systems). Since the identification model of the system contains the product of the parameters of the nonlinear part and the linear part, we use the key variables separation technique and express the output of the system as the linear combination of all parameters, and then derive a Newton iterative identification method. The simulation results show that the proposed algorithm is effective.     

高层建筑风荷载反演研究

本文研究了结构参数未知条件下的高层建筑风荷载反演问题，通过将平均风速的实测特性作为识别计算中的辅助条件，文中提出了一类荷载归一化统计平均方法，结合工程实例，进行了高层建筑风荷载反演分析，结果表明，本文建议方法可以在幅值、时程等方面均给出良好的风荷载反演结果，同时识别得到的结构参数具有良好的精度。从而为高层建筑风荷载研究提供了一条可行途径。     

Newton iterative identification for a class of output nonlinear systems with moving average noises

This paper discusses iterative identification problems for a class of output nonlinear systems (i.e., Wiener nonlinear systems) with moving average noises from input–output measurement data, based on the Newton iterative method. The basic idea is to decompose a nonlinear system into two subsystems, to replace the unknown variables in the information vectors with their corresponding estimates at the previous iteration, and to present a Newton iterative identification method using the hierarchical identification principle. The numerical simulation results indicate that the proposed algorithms are effective.     

Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle

This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input–output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.     

灰色相关性分析在结构静力损伤识别中的应用

基于灰色理论的相关性分析方法，首次提出了灰色曲率关联系数的概念并将其应用到结构的静力损伤识别中，提出了对局部损伤十分敏感的静态位移曲率置信因子SDCACi，通过该因子的大小对各节点所连接的单元是否会发生损伤进行精确的判断，然后运用最小二乘法对损伤区域的损伤程度进行识别．并将该方法应用于两端固支梁的损伤识别中，由识别结果可以证明：不论测量数据(用有限元仿真计算并考虑了测量误差)的多少，该方法对结构中的单损伤和多损伤都能进行准确的定位，因此该方法在大型结构及复杂结构的损伤识别中具有广阔的应用前景．     

Restoring force and dynamic loadings identification for a nonlinear chain-like structure with partially unknown excitations

Most of the currently employed vibration-based identification approaches for structural damage detection are based on eigenvalues and/or eigenvectors extracted from dynamic response measurements, and strictly speaking, are only suitable for linear system. However, the inception and growth of damage in engineering structures under severe dynamic loadings are typical nonlinear procedures. Consequently, it is crucial to develop general structural restoring force and excitation identification approaches for nonlinear dynamic systems because the restoring force rather than equivalent stiffness can act as a direct indicator of the extent of the nonlinearity and be used to quantitatively evaluate the absorbed energy during vibration, and the dynamic loading is an important factor for structural remaining life forecast. In this study, based on the instantaneous state vectors and partially unknown excitation, a power series polynomial model (PSPM) was utilized to model the nonlinear restoring force (NRF) of a chain-like nonlinear multi-degree-of-freedom (MDOF) structure. To improve the efficiency and accuracy of the proposed approach, an iterative approach, namely weighted adaptive iterative least-squares estimation with incomplete measured excitations (WAILSE-IME), where a weight coefficient and a learning coefficient were involved, was proposed to identify the restoring force of the structure as well as the unknown dynamic loadings simultaneously. The response measurements of the structure, i.e., the acceleration, velocity, and displacement, and partially known excitations were utilized for identification. The feasibility and robustness of the proposed approach was verified by numerical simulation with a 4 degree-of-freedom (DOF) numerical model incorporating a nonlinear structural member, and by experimental measurements with a four-story frame model equipped with two magneto-rheological (MR) dampers mimicking nonlinear behavior. The results show the proposed approach by combining the PSPM and WAILSE-IME algorithm is capable of effectively representing and identifying the NRF of the chain-like MDOF nonlinear system with partially unknown external excitations, and provide a potential way for damage prognosis and condition evaluation of engineering structures under dynamic loadings which should be regarded as a nonlinear system.     

Non-probabilistic information fusion technique for structural damage identification based on measured dynamic data with uncertainty

Based on measured natural frequencies and acceleration responses,a non-probabilistic information fusion technique is proposed for the structural damage detection by adopting the set-membership identification(SMI) and twostep model updating procedure.Due to the insufficiency and uncertainty of information obtained from measurements,the uncertain problem of damage identification is addressed with interval variables in this paper.Based on the first-order Taylor series expansion,the interval bounds of the elemental stiffness parameters in undamaged and damaged models are estimated,respectively.The possibility of damage existence(PoDE) in elements is proposed as the quantitative measure of structural damage probability,which is more reasonable in the condition of insufficient measurement data.In comparison with the identification method based on a single kind of information,the SMI method will improve the accuracy in damage identification,which reflects the information fusion concept based on the non-probabilistic set.A numerical example is performed to demonstrate the feasibility and effectiveness of the proposed technique.