基于变分模态分解和天牛须搜索的磁瓦内部缺陷声振检测

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振动与冲击 ›› 2020, Vol. 39 ›› Issue (17) : 124-133.

论文

黄沁元1，谢罗峰2，殷国富2，冉茂霞1，刘鑫1

作者信息 +

Acoustic-vibration detection for internal defects of magnetic tile based on VMD and BAS

HUANG Qinyuan1, XIE Luofeng2, YIN Guofu2, RAN Maoxia1, LIU Xin1

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文章历史 +

摘要

在磁瓦内部缺陷声振检测中，具备可预设尺度和自适应能力的变分模态分解（Variational Mode Decomposition，VMD）在处理相关声振信号时具有明显优势。然而VMD的参数选择范围广且对分解效果影响大，为解决参数的统一预设问题，提出一种基于天牛须搜索（Beetle Antennae Search，BAS）的VMD参数优化方法，并应用于磁瓦内部缺陷检测。该方法首先根据声振信号特点建立基于模态能量和模态中心频率变化规律的目标函数；再利用BAS在参数空间中搜寻目标函数最大值，所需的VMD统一预设参数即为该最大值所对应的参数组合；最后，在该优化参数下对逐个信号进行VMD处理并提取模态中心频率特征，进而由支持向量机完成特征识别。实验结果表明，所提出的方法能有效优化VMD参数并实现磁瓦内部缺陷的快速精准检测。

Abstract

Variational mode decomposition (VMD) with not only preset scale but also self-adaptable ability possesses a significant advantage in processing the acoustic impact signal for detecting the internal defect in arc magnet using acoustic impact method. However, the performance of VMD extremely depends on the parameters selection, while these parameters have considerable quantity and wide range. In order to obtain consistent VMD parameters that are suitable for all acoustic impact signals, a novel VMD parameter optimization with beetle antennae search (BAS) algorithm was presented. Firstly, an objective function based on both mode energy distribution and mode center frequency variation was designed by the signal characteristics. Secondly, BAS was executed to search for the objective function maximum from the VMD parameter space. The parameters corresponding to the maximum were selected as the optimal VMD parameters. Finally, the signals were processed by VMD with these optimal parameters, and then the mode center frequencies associated with the internal defect were extracted as the signal features. The classifier based on support vector machine was employed for the feature identification. The experimental results demonstrate that the proposed method can efficiently optimize VMD parameters and realize a rapid and accurate detection of the internal defect in arc magnet.

导出引用

黄沁元1，谢罗峰2，殷国富2，冉茂霞1，刘鑫1. 基于变分模态分解和天牛须搜索的磁瓦内部缺陷声振检测[J]. 振动与冲击, 2020, 39(17): 124-133

HUANG Qinyuan1, XIE Luofeng2, YIN Guofu2, RAN Maoxia1, LIU Xin1. Acoustic-vibration detection for internal defects of magnetic tile based on VMD and BAS[J]. Journal of Vibration and Shock, 2020, 39(17): 124-133

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