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The empirical mode decomposition (EMD) combines with Hilbert Transform is a common method of nonlinear and non-stationary signal time frequency analysis. Signal can be decomposed into different intrinsic mode functions (IMF) through the EMD. Each IMF represents a simple oscillation which provides meaningful instantaneous frequency through Hilbert transform. The mode mixing of IMF is a critical problem...
The Bivariate Empirical Mode Decomposition (BEMD) is an extension of Empirical Mode Decomposition (EMD) algorithm. In its classical formulation, the EMD can only be applied to real-valued time series. In this paper, the BEMD algorithm is proposed as an alternative to estimate the glottal source from the speech signal. The bivariate empirical mode decomposition decomposes the complex log spectrum into...
This paper proposes a competent method for broken bars fault identification in line-start and inverter-fed industrial induction motors. The basis of the proposed method is to determine different frequency bands of the faulty motor current and extract a proper intrinsic mode function (IMF) developed by empirical mode decomposition (EMD). It is analytically proved that two specific IMFs, ...
The Empirical Mode Decomposition (EMD) is becoming increasingly popular for the multi-scale analysis of signals. However, the data-driven and adaptive nature of the EMD raises concerns regarding the uniqueness of the decomposition as well as the extend to which oscillatory modes can be mixed across different IMFs. This paper proposes a solution to this problem for the analysis of ECG signals. The...
With the rapid development and interconnection of power systems, the low frequency oscillations(LFO) has become one of the serious factors threatening the power system stability. Thus, how to quickly locate the source of LFO and implement controller to suspend it, is an urgent task. The general approach to locate the sources of LFO is using the node contribution factor(NCF), which indicating the contribution...
Biomedical signals are in general non-linear and non-stationary which renders them difficult to analyze with classical time series analysis techniques. Empirical Mode Decomposition (EMD) in conjunction with a Hilbert spectral transform, together called Hilbert-Huang Transform, is ideally suited to extract informative components which are characteristic of underlying biological or physiological processes...
Based on a detailed discussion regarding the attributes of so-called ride waves and the shortages of the classical empirical mode decomposition, this paper presents an improved method. A key point of the proposed method is that the ride waves in a signal should been first removed as independent components before the classical empirical mode decomposition is conducted. Experiments have shown that the...
The envelope analysis technique, used for many years in faults detection of bearing by vibration analysis, is combined with the technique of empirical mode decomposition (EMD), which has been applied to non-stationary and non-linear signal analysis. The method to fault diagnosis of bearing based on EMD and envelope spectrum is presented. The methodology developed in this paper decomposes the original...
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