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首页> 外文期刊>Journal of vibration and control: JVC >An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture
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An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture

机译：用于单通道振动信号混合的盲分量分离的改进的经验模态分解方法

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摘要

Blind component separation aims to decompose a single-channel vibration signal mixture into periodic components and random transient components. In addition to periodic components, random transient components with a high degree of impulsiveness are signals of interest in practice. An adaptive signal processing method called empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into the sum of simple components termed intrinsic mode functions (IMFs). Ensemble empirical mode decomposition (EEMD) is an improvement of EMD and aims to relieve a mode mixing problem that exists in EMD. However, there is no universal standard formula that can be used to select appropriate parameters of EEMD. Improper parameters of EEMD still cause a mode mixing problem that makes a signal of a similar scale reside in some successive IMFs. An enhanced EEMD for the purpose of blind component separation is developed in this paper to respectively extract periodic components and random transient components from a single-channel vibration signal mixture. A revised spectral coherence is proposed to measure the spectral dependence between two successive IMFs. The closer the revised spectral coherence is to one, the higher the spectral dependence of two successive IMFs is. Additionally, a fusion rule based on locations of local minima of the revised spectral coherence is proposed to automatically fuse successive IMFs with similar characteristics into a new IMF, called an enhanced IMF (EIMF). Vibration signals including simulated and real multi-fault signals are used to verify the enhanced EEMD. A comparison with EEMD is conducted to show the superiority of the enhanced EEMD. The results demonstrate that the enhanced EEMD has better performance than EEMD for automatically extracting periodic components and random transient components from a single-channel vibration signal mixture.
机译：盲分量分离的目的是将单通道振动信号混合物分解为周期性分量和随机瞬态分量。在实践中，除了周期性分量外，具有高度冲动性的随机瞬态分量也是令人关注的信号。一种称为经验模式分解（EMD）的自适应信号处理方法，将非线性和非平稳信号分解为称为固有模式函数（IMF）的简单成分之和。集成经验模式分解（EEMD）是EMD的改进，旨在缓解EMD中存在的模式混合问题。但是，没有通用的标准公式可用于选择EEMD的适当参数。 EEMD的参数不正确仍然会导致模式混合问题，使类似比例的信号驻留在某些连续的IMF中。本文开发了一种用于盲分量分离的增强EEMD，以分别从单通道振动信号混合物中提取周期性分量和随机瞬态分量。提出了修订的频谱相干性以测量两个连续IMF之间的频谱相关性。修正后的光谱相干性越接近一，两个连续IMF的光谱依赖性就越高。另外，提出了一种基于修正频谱相干的局部最小值位置的融合规则，以将具有相似特征的连续IMF自动融合到新的IMF中，称为增强IMF（EIMF）。包括模拟和实际多故障信号的振动信号用于验证增强型EEMD。与EEMD进行了比较，以显示增强型EEMD的优越性。结果表明，增强型EEMD具有比EEMD更好的性能，可从单通道振动信号混合中自动提取周期分量和随机瞬变分量。

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