Abstract This paper proposes a non-stationary signal decomposition approach to remove the harmonic responses in the operational modal analysis for time-varying structures. Two time–frequency representations, the non-parametric windowed Fourier transform and the power spectral density estimated by the parametric functional series time-dependent autoregressive moving average model, are combined to decompose the raw multimodal responses into individual unimodal components. According to the histogram shape or the excess Kurtosis value of the components, the harmonic components and structural components can be distinguished and the harmonic ones will be removed in the modal identification. To identify the close modes of a time-varying structure, a time–frequency domain decomposition method based on the power spectral density matrix and singular value decomposition technique is also proposed. The numerical and experimental examples finally demonstrate that the proposed approach can remove the harmonic components effectively and can obtain good modal parameters identification results for time-varying structures with close or even repeated modes.

中文翻译：

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操作模态分析中谐波响应检测的非平稳信号分解方法

摘要 本文提出了一种非平稳信号分解方法来消除时变结构运行模态分析中的谐波响应。两个时频表示，非参数加窗傅立叶变换和由参数函数序列时间相关自回归移动平均模型估计的功率谱密度，结合起来将原始多峰响应分解为单个单峰分量。根据分量的直方图形状或超峰度值，可以区分谐波分量和结构分量，并在模态识别中去除谐波分量。为了识别时变结构的闭合模式，提出了一种基于功率谱密度矩阵和奇异值分解技术的时频域分解方法。数值和实验实例最终表明，所提出的方法可以有效去除谐波分量，并且对于具有接近甚至重复模态的时变结构可以获得良好的模态参数识别结果。