scholarly journals Fast Ensemble Empirical Mode Decomposition Using the Savitzky Golay filter

2021 ◽  
Vol 1 (1) ◽  
pp. 79-86
Author(s):  
WAHIBA MOHGUEN ◽  
RAÏS EL’HADI BEKKA
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Computing Time ◽  
Ensemble Empirical Mode Decomposition ◽  
Mixing Effect ◽  
Mode Decomposition ◽  
Reconstructed Signal ◽  
Stationary Signals ◽  
Very High ◽  
Mode Mixing

Empirical mode decomposition (EMD) is a powerful algorithm proposed to analysis of nonlinear and non-stationary signals. The phenomenon of mode mixing is one of the major disadvantages of the EMD. The Ensemble EMD (EEMD) was introduced to eliminate the mode-mixing effect. The principle of EEMD is to add additional white noise into the signal with many trials. The noise in each trial is different; and the added noise can be completely cancelled out on average, if the number of trials is very high. The number of trials is a high computational load. The improvement on computational efficiency of EEMD is therefore required. In this paper, an improvement on the computing time of the EEMD was proposed by replacing white noise with white noise filtered using Savitzky-Golay (SG) filter. Numerical simulations were performed to demonstrate that such replacement has effectively reduced the number of trials to obtain a noise-free reconstructed signal.

scholarly journals Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaohang Zhou ◽  
Deshan Shan ◽  
Qiao Li
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Measured Signal ◽  
Morphological Filter ◽  
Structuring Element ◽  
Mode Decomposition ◽  
Additional Mode ◽  
Adaptive Ability ◽  
Mode Mixing

In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.

scholarly journals The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bin Liu ◽  
Peng Zheng ◽  
Qilin Dai ◽  
Zhongli Zhou
Keyword(s):  
Spectral Analysis ◽  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Mode Splitting ◽  
Mode Decomposition ◽  
Microseismic Signal ◽  
Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING

2013 ◽  
Vol 05 (03) ◽  
pp. 1350012 ◽  
Author(s):  
RAÏS EL'HADI BEKKA ◽  
YAAKOUB BERROUCHE
Keyword(s):  
Signal Processing ◽  
Numerical Simulations ◽  
Empirical Mode Decomposition ◽  
Computing Time ◽  
Ensemble Empirical Mode Decomposition ◽  
Computational Time ◽  
Nonstationary Signals ◽  
Mode Decomposition ◽  
Mode Mixing

The empirical mode decomposition (EMD) is a useful method for the analysis of nonlinear and nonstationary signals and found immediate applications in diverse areas of signal processing. However, the major inconvenience of EMD is the mode mixing. The ensemble EMD (EEMD) was proposed to solve the problem of mode-mixing with the assistance of added noises producing the residue noise in the signal reconstructed. The residue noise in the IMFs can be reduced with a large number of ensemble trials at the expense of the increase of computational time. Improving the computing time of the EEMD by reducing the number of ensemble trials was thus proposed in this paper by over-sampling the signal to be decomposed. Numerical simulations were conducted to demonstrate proposed approach.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

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

2017 ◽  
Vol 09 (02) ◽  
pp. 1750004 ◽  
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.

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

scholarly journals Analysis of Vehicle Platform Vibration Based on Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chengwu Shen ◽  
Zhiqian Wang ◽  
Chang Liu ◽  
Qinwen Li ◽  
Jianrong Li ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Measurement Accuracy ◽  
Vibration Isolation ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Data ◽  
Mode Decomposition ◽  
Operation Process ◽  
Vehicle Platform ◽  
Mode Mixing

Vehicle platform vibration (VPV) directly affects the measurement accuracy of precise measuring instrument (PMI) fixed on it. In order to reduce the influences of VPV on measurement accuracy, it is necessary to perform vibration isolation between vehicle platform and PMI. Analysis of vibration characteristics is a prerequisite for vibration isolation. However, empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) reveal that there is obvious mode mixing phenomenon in the collected VPV signals. In this paper, a noise stretch ensemble empirical mode decomposition (NSEEMD) method is proposed to suppress mode mixing, and the specific operation process of NSEEMD is expounded. By NSEEMD, mode mixing of the collected platform vibration data is well suppressed, and the principal component of platform vibration can be obtained.

Time–frequency analysis of torsional vibration signals based on the improved complete ensemble empirical mode decomposition with adaptive noise, Robust independent component analysis, and Prony’s methods

2021 ◽  
pp. 107754632110381
Author(s):  
Qingjie Zhang ◽  
Guangxiang Lu ◽  
Chengyu Zhang ◽  
You Xu
Keyword(s):  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Torsional Vibration ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Adaptive Noise ◽  
Prony Analysis

The torsional vibration signals of rotating shafts are multimodal non-stationary noisy signals. The harmonics and attenuation characteristics of these non-stationary signals cannot be obtained effectively by the ordinary short-time Fourier transform algorithm. Although Prony analysis can accurately fit and identify the characteristic coefficients of such non-stationary signals, it is still sensitive to noise. In this article, we propose a system for denoising and identification of torsional vibration signals. In particular, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), wavelet transform, and robust independent component analysis methods are used to denoise the torsional vibration signals, and then, Prony analysis is used to obtain the characteristic parameters of these signals. The proposed algorithm has good denoising performance and it can improve the identification accuracy and reduce the order of the Prony analysis.

Improved EEMD Applied to Rotating Machinery Fault Diagnosis

2011 ◽  
Vol 128-129 ◽  
pp. 154-159 ◽  
Author(s):  
Lue Chen ◽  
Ge Shi Tang ◽  
Yan Yang Zi ◽  
Fei Fan
Keyword(s):  
White Noise ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Precise Diagnosis ◽  
Empirical Mode Decomposition Method ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

Ensemble Empirical Mode Decomposition (EEMD) is a new noise-assisted data analysis (NADA) method. The effect of EEMD depends on two key parameters which are the amplitude of white noise and the ensemble times. However, the shortcoming of EEMD is that it lacks adaptability and reliability because these two key important parameters are obtained by experience and human intervention. An Improved Ensemble Empirical Mode Decomposition method is proposed in this paper, by adding white noise and ascertaining ensemble number adaptively. The criterion of adding white noise in Improved EEMD is established, by which a composite simulation signal could be adaptively and accurately decomposed into IMFs without mode mixing. The proposed method is applied to a gear fault detection of hot strip finishing mills. The result shows that Improved EEMD method successfully extracts the gear fault feature with high precise diagnosis results.

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