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将盲源分离技术应用于结构非线性振动特性分析,在小波软阈值去噪的基础上,用二阶统计量盲辨识(SOBI)算法从结构的振动信号中分离出第一阶振动分量,然后利用Hilbert变换得到频率与振幅的关系。在数值仿真算例的基础上,采用钢筋混凝土简支梁和预应力箱梁的试验数据,验证了在分析结构非线性振动特性时,盲源分离技术抗噪性好,比短时傅氏变换更易操作。结果表明:在较小损伤的情况下,利用非线性振动特性识别结构损伤程度,有时比由基于线性振动的模态识别得到的结构频率改变的方式更可靠。
The blind source separation technology is applied to the analysis of structural nonlinear vibration characteristics. Based on the wavelet soft threshold denoising, the second order statistical blind identification (SOBI) algorithm is used to separate the first order vibration component from the structure vibration signal. Then, Using Hilbert transform to get the relationship between frequency and amplitude. Based on the numerical simulation examples, the experimental data of reinforced concrete simply supported beams and prestressed box girders demonstrate that the blind source separation technique is better in noise immunity than short-time Fourier transform More easy to operate. The results show that in the case of small damage, the use of non-linear vibration characteristics to identify the degree of structural damage is sometimes more reliable than the change of structure frequency from the modal identification based on linear vibration.