BACK PROJECTION STRATEGY FOR SOLVING MODE MIXING PROBLEM

2012 ◽  
Vol 10 (04) ◽  
pp. 1250031
Author(s):  
XIYUAN HU ◽  
SILONG PENG ◽  
JIE TAN ◽  
WEIPING HU
Keyword(s):  
Empirical Mode Decomposition ◽  
Real Life ◽  
Data Driven ◽  
Back Projection ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Data Driven Approach ◽  
Projection Strategy ◽  
Mixing Problem ◽  
Mode Mixing

Empirical mode decomposition (EMD) is an adaptive and data-driven approach for analyzing multicomponent nonlinear and non-stationary signals. The mode-mixing problem is one of the most important topics for the improvement of the EMD algorithm. In this paper, we study the reasons of the mode mixing phenomenon. And then, we propose a new method to resolve this problem relying on the assumption that each IMF should be locally orthogonal to the others. We experiment on several signals, including simulated and real-life signals, to demonstrate the efficacy of the proposed method to resolve the mode mixing problem.

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.

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

2018 ◽  
Vol 10 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Catherine M. Sweeney-Reed ◽  
Slawomir J. Nasuto ◽  
Marcus F. Vieira ◽  
Adriano O. Andrade
Keyword(s):  
Empirical Mode Decomposition ◽  
Data Driven ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

scholarly journals A New Islanding Detection Technique using Ensemble Empirical Mode Decomposition

2020 ◽  
Vol 9 (3) ◽  
pp. 461-466
Keyword(s):  
Empirical Mode Decomposition ◽  
Distributed Generation ◽  
Gaussian White Noise ◽  
Ensemble Empirical Mode Decomposition ◽  
Islanding Detection ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Signal Processing Methods ◽  
Mixing Problem ◽  
Mode Mixing

Penetration of distributed generation (DG) is rapidly increasing but their main issue is islanding. Advanced signal processing methods needs a renewed focus in detecting islanding. The proposed scheme is based on Ensemble Empirical Mode Decomposition (EEMD) in which Gaussian white noise is added to original signal which solves the mode mixing problem of Empirical mode decomposition (EMD) and Hilbert transform is applied to obtained Intrinsic mode functions(IMF). The proposed method reliably and accurately detects disturbances at different events

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Jing Yuan ◽  
Zhengjia He ◽  
Jun Ni ◽  
Adam John Brzezinski ◽  
Yanyang Zi
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Measured Signal ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

PERFORMANCE EVALUATION OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION

2009 ◽  
Vol 01 (02) ◽  
pp. 231-242 ◽  
Author(s):  
R. K. NIAZY ◽  
C. F. BECKMANN ◽  
J. M. BRADY ◽  
S. M. SMITH
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Noise Levels ◽  
Stopping Criteria ◽  
Mode Decomposition ◽  
The Common ◽  
Common Problems ◽  
Mode Mixing

Empirical mode decomposition (EMD) is an adaptive, data-driven algorithm that decomposes any time series into its intrinsic modes of oscillation, which can then be used in the calculation of the instantaneous phase and frequency. Ensemble EMD (EEMD), where the final EMD is estimated by averaging numerous EMD runs with the addition of noise, was an advancement introduced by Wu and Huang (2008) to try increasing the robustness of EMD and alleviate some of the common problems of EMD such as mode mixing. In this work, we test the performance of EEMD as opposed to normal EMD, with emphasis on the effect of selecting different stopping criteria and noise levels. Our results indicate that EEMD, in addition to slightly increasing the accuracy of the EMD output, substantially increases the robustness of the results and the confidence in the decomposition.

A fast online bandwidth empirical mode decomposition scheme for avoidance of the mode mixing problem

2017 ◽  
Vol 232 (20) ◽  
pp. 3652-3674 ◽  
Author(s):  
SH Momeni Massouleh ◽  
Seyed Ali Hosseini Kordkheili ◽  
H Mohammad Navazi
Keyword(s):  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Dominant Frequency ◽  
Auxiliary Function ◽  
Intrinsic Mode Functions ◽  
Online Data ◽  
Decomposition Scheme ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Mode Mixing

The main objective of this work is to propose a scheme to extract intrinsic mode functions of online data with an acceptable speed as well as accuracy. For this purpose, an individual block framework method is firstly employed to extract the intrinsic mode functions. In this method, buffers are selected such that they overlap with their neighbors to prevent the end effect errors with no need for the averaging process. And in order to avoid the mode mixing problem, a bandwidth empirical mode decomposition scheme is developed to effectively improve the results. Through this scheme, an auxiliary function made of both high- and low-frequency components corresponding to noise and dominant frequency is added to data for the strengthening of the components for the better extraction of intrinsic mode functions during sifting process. An index criterion as well as a threshold limit is also introduced to separate high- and low-frequency parts of data at desired frequency range. Advantages of the proposed scheme are assessed and comparisons with the available methods are presented. Solution of different types of examples and experimentally generated data for two faulty ball bearings reveals that the present easily implemented scheme achieves results with lower computational efforts and accuracy.

Bandpass Empirical Mode Decomposition Using a Rolling Ball Algorithm

2015 ◽  
Vol 07 (01n02) ◽  
pp. 1550003 ◽  
Author(s):  
Adam Huang ◽  
Min-Yin Liu ◽  
Wei-Te Yu
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Rolling Ball ◽  
Mode Decomposition ◽  
Local Extrema ◽  
Mixing Problem ◽  
Mode Mixing

We propose using a rolling ball algorithm, which moves a ball along a time series signal, to sort the local extrema within a signal according to a geometric tangibility criterion. Letting the ball always roll above or below the signal, we are able to classify the signal’s extrema according to their tangibility: touched or not touched by the ball. Applying this ball-tangibility information to select an extremum in the empirical mode decomposition (EMD) algorithm, we are able to prevent the mode mixing problem in analyzing intermittent signals and decompose mode functions satisfying bandpass filtering properties.

Spin flight mode identification with OEEMD algorithm

2019 ◽  
Vol 91 (4) ◽  
pp. 582-600
Author(s):  
S. Abolfazl Mokhtari ◽  
Mehdi Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Signal ◽  
Complex Signals ◽  
Content Type ◽  
Mode Decomposition ◽  
Flight Mode ◽  
Highly Nonlinear ◽  
Mixing Problem ◽  
Mode Mixing

Purpose The paper aims to present an innovative method for identification of flight modes in the spin maneuver, which is highly nonlinear and coupled dynamic. Design/methodology/approach To fix the mode mixing problem which is mostly happen in the EMD algorithm, the authors focused on the proposal of an optimized ensemble empirical mode decomposition (OEEMD) algorithm for processing of the flight complex signals that originate from FDR. There are two improvements with the OEEMD respect to the EEMD. First, this algorithm is able to make a precise reconstruction of the original signal. The second improvement is that the OEEMD performs the task of signal decomposition with fewer iterations and so with less complexity order rather than the competitor approaches. Findings By applying the OEEMD algorithm to the spin flight parameter signals, flight modes extracted, then with using systematic technique, flight modes characteristics are obtained. The results indicate that there are some non-standard modes in the nonlinear region due to couplings between the longitudinal and lateral motions. Practical implications Application of the proposed method to the spin flight test data may result accurate identification of nonlinear dynamics with high coupling in this regime. Originality/value First, to fix the mode mixing problem in EMD, an optimized ensemble empirical mode decomposition algorithm is introduced, which disturbed the original signal with a sort of white Gaussian noise, and by using white noise statistical characteristics the OEEMD fix the mode mixing problem with high precision and fewer calculations. Second, by applying the OEEMD to the flight output signals and with using the systematic method, flight mode characteristics which is very important in the simulation and controller designing are obtained.

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