A frequency identification method suitable for all-phase spectral refinement of architectural structures

Abstract Short-time Fourier transformation (STFT) has been widely recognized as an intuitive time-frequency analysis method. However, its application in building structures is constrained by its low accuracy of low-frequency recognition in short data. Accordingly, an all-phase chirp-z transformation (AP-CZT) proposed for frequency recognition in building structures. Specifically, the autoregressive (AR) model, which has a proposed order determination algorithm, is used to solve the problem of data length limitation in the all-phase data process. Subsequently, an algorithm based on absolute inverse proportional function (AIP) is developed for post-refinement frequency correction. To verify its actual application, the ap-CZT method is used to analyze simulating finite element model and white noise feedback data from the actual shake table. The ap-CZT method is proven to be capable of correctly finding high-order frequencies. Moreover, it can accurately identify signal frequencies in short data. Therefore, the ap-CZT method can be applied as a frequency identification method in STFT in the field of building structures.

摘要 短时傅里叶变换（Short-time Fourier Transformation, STFT）是一种被广泛认可的直观时频分析方法。然而，其在建筑结构领域的应用受限于短数据场景下低频识别精度不足的缺陷。为此，本文提出一种面向建筑结构频率识别任务的全相位线性调频z变换（All-phase Chirp-Z Transformation, AP-CZT）方法。具体而言，本文采用搭载阶次确定算法的自回归（AutoRegressive, AR）模型，解决全相位数据处理中的数据长度限制问题；随后提出一种基于绝对反比例函数（Absolute Inverse Proportional Function, AIP）的算法，用于后续的精细化频率校正。为验证该方法的实际应用效果，本文采用AP-CZT方法对仿真有限元模型与实际振动台的白噪声反馈数据开展分析。实验结果证明，AP-CZT方法可准确识别出高阶频率，且能在短数据下精准辨识信号频率。因此，AP-CZT方法可作为建筑结构领域中替代短时傅里叶变换的有效频率识别方法。