基于滑动窗宽优化的LMSSGST识别非平稳信号瞬时频率

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (19) : 183-195.

论文

基于滑动窗宽优化的LMSSGST识别非平稳信号瞬时频率

刘景良1，2，苏杰龙1，戴逸宸1，李宇祖1，黄永2，郑文婷3

作者信息 +

Recognition of non-stationary signal instantaneous frequency withLMSSGST based on sliding window width optimization

LIU Jingliang1,2, SU Jielong1, DAI Yichen1, LI Yuzu1, HUANG Yong2, ZHENG Wenting3

Author information +

文章历史 +

摘要

为提高非平稳响应信号瞬时频率的识别效果，提出基于滑动窗宽优化的局部最大同步挤压广义S变换(Local Maximum Synchrosqueezing Generalized S-Transform, LMSSGST)。该方法首先对非平稳响应信号进行广义S变换获得相应的时频系数；其次，利用该响应信号的功率谱密度特征曲线确定局部最大同步挤压算子中滑动窗的宽度；再次，通过局部最大同步挤压算子进行时频重排；最后，采用模极大值改进算法提取瞬时频率曲线。通过两个数值算例、一个滑动窗宽参数分析和一个时变拉索试验验证了所提方法的有效性，研究结果表明：利用功率谱密度特征曲线能够有效确定滑动窗的窗宽和模极大值算法的提取范围。相比局部最大同步挤压变换算法，基于滑动窗宽优化的LMSSGST具有更佳的瞬时频率识别效果。

Abstract

In order to improve the identification effect of instantaneous frequency from non-stationary response signals, the sliding window width optimization based local maximum synchrosqueezing generalized S-transform (LMSSGST) method is proposed. In this method, the generalized S-transform is performed on the non-stationary response signal at first. Subsequently, the length of the sliding window embedded in the local maximum synchrosqueezing operator is optimized by solving the power spectral density characteristic curve of the target signal. After that, the local maximum synchrosqueezing operator is used to reassign the time-frequency coefficients. Finally, the instantaneous frequency curves can be extracted by the improved modulus maximum method. Two numerical cases, a parameter analysis on sliding window width and a time-varying cable test are investigated to verify the effectiveness and accuracy of the proposed method. The results demonstrate that the index of the power spectral density characteristic curve is beneficial for the selection of the sliding window length and the extraction range of the improved modulus maximum method. Moreover, the sliding window width optimization based LMSSGST method behaves better on instantaneous frequency identification when compared with the current local maximum synchrosqueezing transform algorithm.

导出引用

刘景良1, 2, 苏杰龙1, 戴逸宸1, 李宇祖1, 黄永2, 郑文婷3. 基于滑动窗宽优化的LMSSGST识别非平稳信号瞬时频率[J]. 振动与冲击, 2024, 43(19): 183-195

LIU Jingliang1, 2, SU Jielong1, DAI Yichen1, LI Yuzu1, HUANG Yong2, ZHENG Wenting3. Recognition of non-stationary signal instantaneous frequency withLMSSGST based on sliding window width optimization[J]. Journal of Vibration and Shock, 2024, 43(19): 183-195

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