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Fast Implementation of Orthogonal Empirical Mode Decomposition and Its Application into Singular Signal Detection

机译:正交经验模式分解的快速实现及其在奇异信号检测中的应用

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Since the Empirical Mode Decomposition (EMD) lacks strict orthogonality, the method of Orthogonal Empirical Mode Decomposition is innovationally proposed by this paper. The primary thought of this method is to obtain the Intrinsic Mode Function (IMF) and the residual function by auto-adaptive band-pass filtering. OEMD is proved to preserve strict orthogonality and completeness theoretically in this paper, and an algorithm to implement OEMD fast, IMF binary searching algorithm is built based on the point that the analytical band-pass filtering preserves perfect band-pass feature in the frequency domain. An application into the singular signal detection shows that OEMD successfully conquers mode aliasing, avoids the occurrence of fault mode, and is featured by fast computing speed. Furthermore, it has good performance to analyze non-stationary signal.
机译:由于经验模式分解(EMD)缺乏严格的正交性,因此本文提出了正交经验模式分解的方法。这种方法的主要思想是通过自适应带通滤波获得内在模式函数(IMF)和残余功能。在理论上证明OEMD在理论上,在本文中维护严格的正交性和完整性,并且基于分析带通滤波保留在频域中的完美带通特征的点,构建了快速实现OEMD的算法。在奇异信号检测中的应用程序显示OEMD成功地征收了模式混叠,避免了故障模式的发生,并且通过快速计算速度特征。此外,它具有良好的性能来分析非稳定性信号。

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