VMD多尺度熵和GWO-SVM在扬声器异常声分类中的应用
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西安工程大学电子信息学院 西安,710600

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TN912

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国家自然科学基金青年项(61901347);陕西省教育厅科技计划项目(18JK0342)


Application of VMD-MSE and Support Vector Machine in the Loudspeaker rub & buzz automatic classification
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School of Electronics and Information Xi’an Polytechnic University , Xi’an710048,China

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    摘要:

    针对扬声器异常声分类中异常声特征提取以及分类识别两个关键环节,提出一种基于变分模态分解( Variational Mode Decomposition, VMD )多尺度熵( Multi-scale entropy, MSE )与灰狼算法优化支持向量机( Grey Wolf Optimizer-Support Vector Machines, GWO-SVM )结合的扬声器异常声分类方法。首先,对扬声器声响应信号进行VMD分解得到一系列本征模态函数( Intrinsic Mode Function, IMF ),计算各阶IMF与原始信号的相关系数,然后选择相关系数高的IMF提取该IMF的MSE作为特征向量,最后利用灰狼算法优化支持向量机模型识别故障类型。实验结果表明,在五种状态下扬声器单元分类的识别中,较EMD ( Empirical Mode Decomposition, EMD )多尺度熵、VMD多尺度散布熵(Multiscale Dispersion Entropy,MDE)、EMD多尺度散布熵的特征提取方法,VMD多尺度熵呈现出更高的识别准确率,其识别准确率为99.3%。能更好地表征异常声特征。

    Abstract:

    Aiming at the two key links of loudspeaker fault diagnosis and fault recognition in the process of loudspeaker rub & buzz automatic classification, an automatic classification method of loudspeaker rub & buzz based on Variational Mode Decomposition (VMD) multiscale entropy (MSE) and Grey Wolf Optimizer-Support Vector Machines is proposed. First, the radiated acoustical signals of loudspeaker units were decomposed by VMD, and calculate the correlation coefficient of each intrinsic mode function (IMF) with the original signal. Then, select the IMF component with high correlation coefficient to extract the multi-scale entropy as the feature vector. Finally, the loudspeaker rub & buzz was judged by GWO-SVM. The experimental results show that, compared with the EMD (EMD) multi-scale entropy, VMD multiscale dispersion entropy (MDE), and EMD multiscale dispersion entropy, VMD multi-scale entropy has a higher recognition rate,The recognition accuracy rate is 99.3%.VMD multi-scale entropy can more accurately characterize the loudspeaker rub & buzz characteristics of the loudspeaker unit .

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周静雷,丁芳,崔琳. VMD多尺度熵和GWO-SVM在扬声器异常声分类中的应用[J].电子测量技术,2022,45(8):41-47

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  • 在线发布日期: 2024-05-10
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