基于数据驱动自适应变分非线性chirp模态分解的瞬时频率识别

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (20) : 18-25.

论文

基于数据驱动自适应变分非线性chirp模态分解的瞬时频率识别

袁平平1，满镇2，赵周杰1，任伟新3

作者信息 +

Instantaneous frequency identification based on data-driven adaptive variational nonlinear chirp mode decomposition

YUAN Pingping1，MAN Zhen2，ZHAO Zhoujie1，REN Weixin3

Author information +

文章历史 +

摘要

为降低初始瞬时频率和信号噪声对变分非线性chirp模态分解（variational nonlinear chirp mode decomposition，VNCMD）的影响，本文提出了一种基于数据驱动自适应变分非线性chirp模态分解（data-driven adaptive variational nonlinear chirp mode decomposition，DDAVNCMD）方法。通过模态能量占比确定响应信号的模态个数，同时采用导数归一化算法初步估算模态分量的初始瞬时频率，并添加迭代时变滤波器来降低噪声的影响，在此基础上再对响应信号进行VNCMD。通过单分量和多分量解析信号及拉索结构试验对所提方法进行验证。研究结果表明，基于DDAVNCMD的瞬时频率识别方法具有较好的准确性和抗噪性。

Abstract

To reduce the impact of initial instantaneous frequency and signal noise on the variational nonlinear chirp mode decomposition (VNCMD), a data-driven adaptive variational nonlinear chirp mode decomposition (DDAVNCMD) is proposed in this paper. The modal number of the response signal is obtained by the proportion of modal energy, and the derivative normalization algorithm is used to preliminarily estimate the initial instantaneous frequencies of the modal components. An iterative time-varying filter is also added to reduce the noise effect. Based on this, the response signal is then subjected to VNCMD. The proposed method is validated through single-component and multi-component analytic signals, as well as a cable structure experiment. The research results indicate that the instantaneous frequency identification method based on DDAVNCMD has good accuracy and anti-noise performance.

导出引用

袁平平1, 满镇2, 赵周杰1, 任伟新3. 基于数据驱动自适应变分非线性chirp模态分解的瞬时频率识别[J]. 振动与冲击, 2024, 43(20): 18-25

YUAN Pingping1, MAN Zhen2, ZHAO Zhoujie1, REN Weixin3. Instantaneous frequency identification based on data-driven adaptive variational nonlinear chirp mode decomposition[J]. Journal of Vibration and Shock, 2024, 43(20): 18-25

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