Modal Identification by Decomposition Methods

通过分解方法进行模态识别

• 批准号: 0727838
• 负责人: Brian Feeny
• 金额: $ 17万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0727838&HistoricalAwards=false
• 关键词: Modal; Identification; Decomposition; Methods

This work is in the area of structural vibrations. New methods will be developed for interpreting test data in order to understand structural vibration properties, with potential applications to aerospace, civil, and mechanical systems.The goal of this proposal is to develop decomposition methods for performing experimental modal analysis. The decomposition methods are performed without measured input signals, which broadens their appeal for modal analysis. The work involves the new state-variable modal decomposition (SVMD), the proper orthogonal decomposition (POD) and the smooth orthogonal decomposition (SOD). In the proposed SVMD, a data-based eigenvalue problem is constructed and related to the generalized eigenvalue problem associated with free-vibration solutions of the state-variable formulation of linear multi-degree-of-freedom systems. The eigenvalues lead to estimates of frequencies and modal damping. The eigenvectors lead to estimates of the mode shapes. The interpretation holds for linear systems with multi-modal freeresponses, whether damping is large or small, modal or nonmodal, and without the need of input data. The connection of the decomposition to the state-variable differential equations provides insight into the application under random excitation. This insight carries over to SOD. Also, the POD is developed to accommodate general mass distributions.Decomposition methods are easy, and need no measured inputs, enabling engineers to master the process with very little learning curve, using basic packages of numerical software, such as Matlab. Since measured inputs are not needed, the process will be enabled on flexible structures and also inaccessible processes, for example the vibrations of a bridge under random excitation of traffic or wind. The extension of these tools to random excitation further broadens the applicability, and accommodates, for example, the turbulence loading on an airplane wing in the wind tunnel or during flight. The direct modal damping estimations of the SVMD are applicable to structures with larger damping than most current approaches. The project will support a doctoral student to learn state-variable modeling, random vibration, signal processing, and experimental instrumentation, in addition to course-work learning required in the doctoral program. Under-represented minorities, women, and economically disadvantaged students will be sought through the MSU Engineering Diversity Office. An undergraduate student will assist in setting up, instrumenting and running experiments. The PI will take part in an MSU summer program for middle schoolers, Mathematics Science and Technology (MST), by co-teaching a 'Mechanical Engineering' class.

这项工作是在结构振动领域。 将开发用于解释测试数据的新方法，以了解结构振动的特性，并在航空航天，民用和机械系统中使用潜在的应用。该提案的目的是开发用于执行实验模态分析的分解方法。 分解方法是在没有测量的输入信号的情况下执行的，这扩大了其对模态分析的吸引力。 这项工作涉及新的状态变量模态分解（SVMD），正交分解（POD）和光滑的正交分解（SOD）。 在拟议的SVMD中，构建了基于数据的特征值问题，并与与现状可变量的自由振动解决方案相关的广义特征值问题有关，该解决方案是线性多样化系统的线性公式。特征值导致频率和模态阻尼的估计。 特征向量导致模式形状的估计。 该解释适用于具有多模式的自由式启动的线性系统，无论阻尼是大还是小，模态还是非模式，并且不需要输入数据。 在随机激发下，分解与状态变化微分方程的连接为应用程序提供了洞察力。这个洞察力延续到草皮。 此外，开发了POD以适应一般的质量分布。分组方法很容易，不需要测量的输入，使工程师能够使用MATLAB等数值软件的基本软件包，使工程师几乎没有学习曲线来掌握该过程。 由于不需要测量的输入，因此将在灵活的结构和无法访问的过程中启用该过程，例如在随机激发交通或风中，桥梁的振动。 这些工具以随机激发的扩展进一步扩大了适用性，并容纳了风洞或飞行过程中飞机机翼上的湍流载荷。 SVMD的直接模态阻尼估计适用于比大多数当前方法更大的阻尼结构。 该项目还将支持博士生学习，除了博士课程中所需的课程学习外，还将学习陈述可变的建模，随机振动，信号处理和实验仪器。将通过MSU工程多样性办公室寻求代表性不足的少数民族，妇女和经济弱势群体的学生。 本科生将协助建立，仪器和运行实验。 PI将通过共同讲授“机械工程”课程，参加中学生，数学科学技术（MST）的MSU夏季计划。