Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms

Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.

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SEPARATION OF TWIN FETAL ECG FROM MATERNAL ECG USING EMPIRICAL MODE DECOMPOSITION TECHNIQUES

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237217500429 ◽

2017 ◽

Vol 29 (06) ◽

pp. 1750042 ◽

Cited By ~ 2

Author(s):

Marjan Salmanvandi ◽

Zahra Einalou

Keyword(s):

Empirical Mode Decomposition ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Principal Component ◽

Ensemble Empirical Mode Decomposition ◽

Sampling Frequency ◽

Decomposition Techniques ◽

Fetal Ecg ◽

Mode Decomposition ◽

Adaptive Noise

In this study, by using a combination of standard Empirical Mode Decomposition (EMD), Ensembling Empirical Mode Decomposition (EEMD), Completing Empirical Mode Decomposition (CEMD) and Principal Component Analysis (PCA), a new method was introduced to separate twin fetal heart rate (FHR) from maternal ECG. The data which were the results of modeling fetal and maternal ECG which be longed to 10 mothers with a sampling frequency of 250[Formula: see text]Hz. In this method, first R-wave of maternal ECG was determined, and then maternal QRS is removed. Further, to clarify these changes and increase resistance to environmental noises, PCA was used. In the next step, all FHRs related to twin fetuses were extracted from signals. Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was used for denoising. By using the proposed method for noise with an amplitude of over 10 dB, the FHR of the first and second (if any) fetuses were separated from maternal ECG with an accuracy of 93.3% and 91.1% respectively. The goal was to improve signal processing dimensions of fetal ECG and provides deeper insight about this issue using EEMD technique. It was tested on a twin fetus with the results suggesting its effectiveness even with increased number of fetuses with slight modifications.

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Diagnosis of mechanical defects using a hybrid method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and optimized wavelet multi-resolution analysis (OWMRA): experimental study

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-020-06496-z ◽

2021 ◽

Author(s):

Mohamed Khemissi Babouri ◽

Nouredine Ouelaa ◽

Tarek Kebabsa ◽

Abderrazek Djebala

Keyword(s):

Experimental Study ◽

Empirical Mode Decomposition ◽

Hybrid Method ◽

Ensemble Empirical Mode Decomposition ◽

Mode Decomposition ◽

Multi Resolution Analysis ◽

Resolution Analysis ◽

Adaptive Noise

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Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x17500048 ◽

2017 ◽

Vol 09 (02) ◽

pp. 1750004 ◽

Cited By ~ 1

Author(s):

Pawel Rzeszucinski ◽

Michal Juraszek ◽

James R. Ottewill

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Diagnostic Information ◽

Optimal Parameters ◽

Vibration Data ◽

Mode Decomposition ◽

Ultrasound Signal ◽

Diagnostic Capabilities ◽

Mixing Problem ◽

Mode Mixing

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

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