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Showing 2 results for Momeni Massouleh
Seyyed Ali Hosseini Korkhili, Hossein Mohammad Navazi, Seyyed Hassan Momeni Massouleh,
Volume 16, Issue 12 (2-2017)
Abstract
The empirical mode decomposition method is a new technique to obtain constitutive components of a signal. Applicability to all kinds of signals including non-stationary and nonlinear is a main feature of this method. So far, many researches have been done in the literature to eliminate or reduce effects of multiple sources of errors such as stop criteria, end effects and interpolation function. This article focuses on end effects error which many of previous solutions have been proposed based on symmetry or similar methods to decline it. The proposed combined method using auto-regressive (AR) models for short sections of signal edges, forecasts tails of maximum and minimum envelops. Some of first intrinsic mode functions are initially calculated as a result of AR model application. The methods based on symmetry are then used to continue sifting algorithm for remaining signal that has no enough extremums to employ AR model. Finally, by executing some examples, more accurate results obtained from proposed method are compared with those achieved from the mirror method. Noise is also added to signal time history in the last example, to simulate a more realistic situation.
S.h. Momeni Massouleh, M. Vesaghati Javan, S.a. Hosseini Kordkheili,
Volume 19, Issue 7 (July 2019)
Abstract
Empirical mode decomposition (EMD) is one of the new methods for decomposing a signal into its constituent components. The existence of multiple error sources has led to activities to eliminate or mitigate their effects. In this research, one of the major problems of EMD for the separation of noise-polluted signals, namely, mode mixing problem has been studied. To solve this problem, bandwidth EMD has been used, which enhances the EMD method and processes speed and greatly prevents mode mixing problem. Also, among the available methods to extract the instantaneous properties, the proper pair of instantaneous properties identification and signal normalization method is presented by an example. To investigate the efficiency of the bandwidth EMD method, using the optimal method of extracting the instantaneous properties, the experimental data of a faulty bearing have been studied and the instantaneous properties of both EMD method and the bandwidth EMD method have been extracted. Using the coefficient of variation criterion, it is shown that the bandwidth EMD method has a higher resolution and better results than EMD method. Finally, using information of decomposed white noise by EMD, the noise isolation quality of the original data is examined, which indicates a better decomposition of the results of the bandwidth EMD method.
