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Research on the Method of Features Extraction for Non-stationary Signal Based onLocal Mean Decomposition

机译:基于非静止信号的特征提取方法研究基于识别均值分解的特征

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For solving the problem of features extraction on non-stationary transient impact signal, a new method of features extraction, namely, the local mean decomposition (LMD),is introducedin this paper.The new method decomposes such signals into a set of functions, each of which is the product of an envelope signal and a frequency modulated signal from which a time-varying instantaneous frequency can be derived. The LMD method can be used to analyse non-stationary transient impact signal. Compared with wavelet decomposition, this algorithm not only avoids the frequency overlapping in the wavelet decomposition, but also overcomes the difficulty in selecting elementary wavelet. The numerical simulation shows the feasibility of the method. The example analysis of transient signal shows the reliability of the method.
机译:为了解决在非静止瞬态冲击信号上提取的特征提取问题,提取的新方法,即局部平均分解(LMD),是本文的。新方法将这些信号分解为一组函数,每个功能其中,其是信封信号的乘积和频率调制信号,可以从中导出时变瞬时频率的频率调制信号。 LMD方法可用于分析非静止瞬态冲击信号。与小波分解相比,该算法不仅避免了小波分解中的频率重叠,而且还克服了选择基本小波的难度。数值模拟显示了该方法的可行性。瞬态信号的示例分析显示了该方法的可靠性。

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