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Constrained mode decomposition method for modal parameter identification
Structural Control and Health Monitoring ( IF 4.6 ) Pub Date : 2021-11-01 , DOI: 10.1002/stc.2878
Jilin Hou 1 , Dengzheng Xu 1 , Łukasz Jankowski 2 , Yajuan Liu 1
Affiliation  

Many mode decomposition methods suffer from aliasing effects and modal distortion. This paper proposes a constrained mode decomposition (CMD) method that directly addresses these problems. The CMD is based on a linear combination of structural-free responses. The decomposed response is thus ensured to have a physical meaning and to satisfy the structural equation of motion, which improves the accuracy of mode decomposition and identification. The decomposition aim is to obtain a single-mode response. The CMD defines the corresponding natural frequency as the target frequency, while other natural frequencies are defined as constrained frequencies. The proposed method combines the measured physical responses in such a way that the constrained frequency components are selectively suppressed, while the amplitude of the target frequency component is selectively retained above a predefined level. The result is the intended single-mode free response, which can be used to clearly extract the corresponding modal parameters. For well-separated modes, the criterion for selective suppression is based on the fast Fourier transform (FFT) peak amplitude. For separation of closely spaced modes, a criterion based on FFT derivative is proposed to avoid modal distortion. The accuracy and applicability of the CMD method is tested in a numerical simulation and using a four-story lab frame structure. The experimental data are used to verify the effectiveness of the proposed CMD method and to compare it with two other widely used mode decomposition methods.

中文翻译:

模态参数辨识的约束模态分解方法

许多模态分解方法存在混叠效应和模态失真。本文提出了一种直接解决这些问题的约束模式分解(CMD)方法。CMD 基于无结构响应的线性组合。从而保证分解后的响应具有物理意义,满足运动的结构方程,提高了模态分解识别的准确性。分解的目的是获得单模响应。CMD 将相应的固有频率定义为目标频率,而将其他固有频率定义为约束频率。所提出的方法结合了测量的物理响应,从而选择性地抑制了受约束的频率分量,而目标频率分量的幅度选择性地保持在预定水平之上。结果是预期的单模自由响应,可用于清晰地提取相应的模态参数。对于分离良好的模式,选择性抑制的标准基于快速傅里叶变换 (FFT) 峰值幅度。为了分离紧密间隔的模态,提出了一种基于 FFT 导数的准则来避免模态失真。CMD 方法的准确性和适用性通过数值模拟和四层实验室框架结构进行了测试。实验数据用于验证所提出的 CMD 方法的有效性,并将其与其他两种广泛使用的模式分解方法进行比较。可以清晰的提取出对应的模态参数。对于分离良好的模式,选择性抑制的标准基于快速傅里叶变换 (FFT) 峰值幅度。为了分离紧密间隔的模态,提出了一种基于 FFT 导数的准则来避免模态失真。CMD 方法的准确性和适用性通过数值模拟和四层实验室框架结构进行了测试。实验数据用于验证所提出的 CMD 方法的有效性,并将其与其他两种广泛使用的模式分解方法进行比较。可以清晰的提取出对应的模态参数。对于分离良好的模式,选择性抑制的标准基于快速傅里叶变换 (FFT) 峰值幅度。为了分离紧密间隔的模态,提出了一种基于 FFT 导数的准则来避免模态失真。CMD 方法的准确性和适用性通过数值模拟和四层实验室框架结构进行了测试。实验数据用于验证所提出的 CMD 方法的有效性,并将其与其他两种广泛使用的模式分解方法进行比较。提出了一种基于 FFT 导数的准则来避免模态失真。CMD 方法的准确性和适用性通过数值模拟和四层实验室框架结构进行了测试。实验数据用于验证所提出的 CMD 方法的有效性,并将其与其他两种广泛使用的模式分解方法进行比较。提出了一种基于 FFT 导数的准则来避免模态失真。CMD 方法的准确性和适用性通过数值模拟和四层实验室框架结构进行了测试。实验数据用于验证所提出的 CMD 方法的有效性,并将其与其他两种广泛使用的模式分解方法进行比较。
更新日期:2021-11-01
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