基于MUSIC算法特征值损伤因子的板状结构损伤程度评估
Damage level evaluation of plate-like structures based on MUSIC algorithm eigenvalue damage factor
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摘要: 研究了多重信号分类(MUSIC)算法在板状结构损伤检测中的应用,提出一种基于MUSIC算法特征值的损伤因子,为基于MUSIC算法的板状结构损伤成像技术提供了一种可靠的损伤程度评估理论。首先利用MUSIC算法计算的高精度特征值和Lamb波损伤散射信号幅值的相关性,采用Abaqus有限元仿真软件模拟不同程度的损伤,将板状结构中的损伤成像定位之后,根据散射信号将结构损伤程度转化为特征值变化量,根据特征值计算损伤因子,建立损伤评估模型预测损伤程度,并通过试验验证其正确性。试验结果表明,在合适的激励频率下,特征值损伤因子随着损伤程度的增加呈现出线性变化,能较好地反映损伤程度;该方法具有较高的准确性和稳定性,在一定损伤程度内能够有效地反映结构损伤程度。Abstract: The application of MUSIC algorithm in plate-like structures was investigated, and a damage factor based on the characteristic value of the MUSIC algorithm was proposed, which provided a reliable damage level assessment theory for plate-like structure damage imaging technology based on the MUSIC algorithm. The correlation between high-precision eigenvalues calculated by the MUSIC algorithm and amplitudes of Lamb wave damage scattering signal was used. The various damage levels were simulated by using the Abaqus finite element software. After the damage was imaged and localized in the plate-like structure, the structural damage level was transformed into the amount of eigenvalue change according to the scattering signal amplitudes, and the damage factor was calculated according to the eigenvalues. A damage evaluation model was established to predict the damage level and the correctness of the model was verified through experiments. Experimental results showed the proposed eigenvalue-based damage factor responded well to the damage level as the damage level increased linearly with the increase of the eigenvalue damage factor under the appropriate excitation frequency. The method, which can effectively reflect the structural damage level within a certain range, had high accuracy and stability, improving the structural damage detection technology based on the MUSIC algorithm.