首页> 外文会议>IMAC Conference on Structural Dynamics >Automated Modal Analysis Based on Statistical Evaluation of Frequency Responses
【24h】

Automated Modal Analysis Based on Statistical Evaluation of Frequency Responses

机译:基于频率响应统计评估的自动模态分析

获取原文

摘要

This paper presents a newly developed method for obtaining the modal model with a proper model order from experimental frequency response functions (FRF). The method is a multi-step procedure which commences with the identification of a high-order state-space model, Exhaustive Model (EM), using the full FRF data set. Then, modal states that give small contribution to the output, quantified by a metric associated to the observability grammian, are rejected from the EM resulting in a Reference Model (RM). Competing models, with the same model order as the RM, are then found by bootstrapping realization using same-size fractions of the full FRF. Eigensolutions of the Bootstrapping Models (BMs) are then paired by the eigensolutions of the RM based on high Modal Observability Correlation (MOC) indices. In a second reduction stage, the modal states with low MOC index are rejected from the BMs. Final model is found by an averaging through BMs. Only one threshold quantity, related to observability grammians need to be set by the user. The method thus requires very little user interaction. The method is applied to experimental data used in a previous IMAC Round Robin exercise for experimental modal analysis evaluation.
机译:本文介绍了从实验频率响应函数(FRF)的正确模型顺序获取模型模型的新开发方法。该方法是使用完整FRF数据集的高阶状态空间模型,识别尺寸的高阶状态空间模型,穷举模型(EM)的多步骤。然后,向由与可​​观察性词子相关联的度量量化的输出给出的贡献的模态状态从EM拒绝导致参考模型(RM)。通过使用完整FRF的相同大小分数,通过自动启动实现与RM相同的竞争模型。然后,基于高模态可观察性相关(MOC)指数,通过RM的EIGensolutions对引导模型(BMS)的突出度。在第二减少阶段,从BMS中拒绝具有低MoC指数的模态状态。通过BMS的平均来发现最终模型。用户只需要设置一个与可观察性语法的阈值数量。因此,该方法需要非常小的用户交互。该方法应用于以前IMAC循环练习的实验数据,用于实验模拟分析评估。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号