Fully automated operational modal identification based on scale-space peak picking algorithm and power spectral density estimation

情态动词 比例(比率) 光谱密度 鉴定(生物学) 计算机科学 工作模态分析 空格(标点符号) 算法 密度估算 估计 功率(物理) 数学 模态分析 统计 工程类 物理 地理 声学 材料科学 电信 地图学 系统工程 估计员 生物 操作系统 量子力学 振动 植物 高分子化学
作者
Xiao Li,Yu-Xia Dong,Feng‐Liang Zhang
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (7): 076206-076206 被引量:4
标识
DOI:10.1088/1361-6501/ad3a8d
摘要

Abstract Modal analysis is a fundamental and essential research direction in the field of structural engineering. The ultimate goal is to determine the modal parameters of the structures. However, the existing modal analysis algorithms often require a large number of parameter adjustments and manual intervention during operation, which cannot be fully automated. In order to realize the automatic identification of modal parameters, the automatic operational modal identification method (AOMI) is proposed based on the interpolated power spectral density estimation (IPSE). To achieve more precise spectrum analysis in the low-frequency band, IPSE is employed to perform Fourier transform on the original frequency domain segment with optimized frequency resolution. This enhances the sharpness of the obtained spectrum in the low-frequency range, making peak frequencies more discernible. Subsequently, the scale-space peak picking algorithm is used to automatically obtain the peak of the power spectral density (PSD), thus enabling the automatic identification of the natural frequency. Finally, the frequency domain decomposition method (FDD) is used to identify modal parameters based on the natural frequencies. The effectiveness of AOMI is verified through the modal identification of the old steel truss bridge and the three layer framework. Under the environmental excitation, the frequencies identified by the IPSE method is close to that of FDD, Bayesian fast fourier transform (FFT) and covariance driven stochastic subspace identification (SSI-COV). Furthermore, the PSD obtained through IPSE has sharper peak than that of FDD and the Welch’s method. The damping ratio identification accuracy and modal assurance criterion (MAC) are satisfactory in AOMI, which can improve the automatic performance.
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