EMD Analysis of Power Harmonics

2013 ◽  
Vol 706-708 ◽  
pp. 1331-1334
Author(s):  
Kai Tuo Zhang ◽  
Ming Li
Keyword(s):  
Power System ◽  
Harmonic Analysis ◽  
Empirical Mode Decomposition ◽  
Single Phase ◽  
Signal Wave ◽  
Harmonic Frequency ◽  
Mode Function ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Power Harmonics

It can obtain intrinsic mode function (IMF) of signal wave with empirical mode decomposition (EMD) in harmonic analysis of power system. Harmonic frequency, amplitude and duration can be obtained through analysis of IMFs. Through EMD analysis on distortion waveform of single-phase AC inverter output as an example, combined with applied scene of distorted voltage, cause of distortion waveform can be deduced. The result shows that EMD analysis on non-stationary signals is of good performance, and a new substitute method of FFT transform in harmonic analysis.

Study on Vibration Signal Marginal Spectrum of Paper-Transferring System in Printing Press Based on EMD

2010 ◽  
Vol 139-141 ◽  
pp. 2464-2468
Author(s):  
Yi Ming Wang ◽  
Shao Hua Zhang ◽  
Zhi Hong Zhang ◽  
Jing Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Design ◽  
Impact Load ◽  
Vibration Signal ◽  
Key Factors ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Periodic Signals ◽  
The Moment

The precision of transferring paper is key factors to decide the print overprint accuracy, and vibration has an important impact on paper transferring accuracy. Empirical mode decomposition (EMD) can be used to extract the features of vibration test signal. According to the intrinsic mode function (IMF) by extracted, it is useful to analyze the dynamic characteristics of swing gripper arm on motion state. Due to the actual conditions of printing, the vibration signal of Paper-Transferring mechanism system is complex quasi periodic signals. Hilbert-Huang marginal spectrum that is based on empirical mode decomposition can solve the problem which is modals leakage by FFT calculated in frequency domain. Through the experimental research, the phase information of impact load at the moment of grippers opening or closing, which can be used for the optimization design of Paper-Transferring system and the improvement in the accuracy of swing gripper arm.

Adaptive stochastic resonance in bistable system driven by noisy NLFM signal: phenomenon and application

2021 ◽  
Vol 379 (2192) ◽  
pp. 20200239 ◽  
Author(s):  
Chen Yang ◽  
Jianhua Yang ◽  
Dengji Zhou ◽  
Shuai Zhang ◽  
Grzegorz Litak
Keyword(s):  
Stochastic Resonance ◽  
Bistable System ◽  
Nonlinear Frequency ◽  
Bearing Fault Diagnosis ◽  
Mode Function ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Fourier Transform Spectrum ◽  
Short Time ◽  
Sr Method

The stochastic resonance (SR) in a bistable system driven by nonlinear frequency modulation (NLFM) signal and strong noise is studied. Combined with empirical mode decomposition (EMD) and piecewise idea, an adaptive piecewise re-scaled SR method based on the optimal intrinsic mode function (IMF), is proposed to enhance the weak NLFM signal. At first, considering the advantages of EMD for dealing with non-stationary signals, the segmented NLFM signal is processed by EMD. Meanwhile, the cross-correlation coefficient is used as the measure to select the optimal IMF that contains the NLFM signal feature. Then, the spectral amplification gain indicator is proposed to realize the adaptive SR of the optimal IMF of each sub-segment signal and reconstruct the enhanced NLFM signal. Finally, the effectiveness of the proposed method is highlighted with the analysis of the short-time Fourier transform spectrum of the simulation results. As an application example, the proposed method is verified adaptability in bearing fault diagnosis under the speed-varying condition that represents a typical and complicated NLFM signal in mechanical engineering. The research provides a new way for the enhancement of weak non-stationary signals. This article is part of the theme issue ‘Vibrational and stochastic resonance in driven nonlinear systems (part 1)’.

Over-Sifting Components Analysis in Bidimensional Empirical Mode Decomposition

2010 ◽  
Vol 159 ◽  
pp. 377-382
Author(s):  
Guang Tao Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Mean ◽  
Components Analysis ◽  
Bidimensional Empirical Mode Decomposition

Define the course of getting mean envelope as an operation (mean envelope operation) in Empirical mode decomposition (EMD), so as to express the Intrinsic Mode Function (IMF) with mean envelopes. Summarize several rules of the mean envelope operation. On this fundamental, the abnormal components exist in the over-sifting IMFs are extracted out, and the conclusion is testified with the infinite sifting experiment.

The Application of Improved HHT to Harmonic Analysis and Fault Detection of Power System

2011 ◽  
Vol 354-355 ◽  
pp. 1406-1411
Author(s):  
Wen Hua Han ◽  
Hai Xia Ren ◽  
Xu Chen ◽  
Xiao Juan Tao
Keyword(s):  
Fault Detection ◽  
Power System ◽  
Harmonic Analysis ◽  
Harmonic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Frequency Domain Analysis ◽  
Analysis Method ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition

Hilbert-Huang transform (HHT) is a new time-frequency-domain analysis method, which is suitable for non-stationary and nonlinear signals. In this paper, endpoint continuation and ensemble empirical mode decomposition (EEMD) decomposition method are introduced to improve the HHT, which solve the endpoint winger and modal aliasing problem. The improved HHT (IHHT) is used for analyzing the harmonic signal and detecting the fault signal of power system. Simulation results show that IHHT is feasible and effective for harmonic analysis and fault detection.

Empirical Mode Decomposition on Geometry Signal Based Spherical Parameterization

2014 ◽  
Vol 998-999 ◽  
pp. 860-863
Author(s):  
Jian Guo Wang ◽  
Qun E ◽  
Ke Ming Yao ◽  
Xin Long Wan
Keyword(s):  
Empirical Mode Decomposition ◽  
Main Idea ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Spherical Parameterization ◽  
Novel Method ◽  
Key Steps

A novel method based onEmpirical Mode Decomposition(EMD) is approached to process the geometry signal. The main idea is to decompose the signal into some different detail components called Intrinsic Mode Function (IMF). The key steps are as follows: First, the signal is spherical parameterization; Second it is transformed into the plane signal and sampled regularly; Third, the translated signal is processed as an image using Bid-Empirical Mode Decomposition, getting several image IMFs; Finally, invert mapping these IMFs to geometry signal and getting the geometry signal’s IMFs.We demonstrate the power of the algorithms through a number of application examples including de-noising and enhancement.

Identification and Cancellation of Pseudo Mode Function in EMD based on Energy Conservation

2016 ◽  
Vol 08 (01) ◽  
pp. 1650001 ◽  
Author(s):  
Dishan Huang ◽  
Xianglong Kong ◽  
Yibing Xia
Keyword(s):  
Experimental Data ◽  
Energy Conservation ◽  
Empirical Mode Decomposition ◽  
Cross Correlation ◽  
Sampling Rate ◽  
Processing Technique ◽  
Post Processing ◽  
Mode Function ◽  
Mode Decomposition ◽  
Signal Mode

This paper introduces an effective method to identify and cancel a pseudo mode function in empirical mode decomposition (EMD) under the condition of insufficient sampling rate. The contents of this paper have three aspects: First, basic properties of the pseudo mode are accurately revealed. Second, a post-processing technique for EMD is developed. This new technique, called as mode function cancellation, can identify and kick out the pseudo mode from the decomposition results, and correct the decomposition error in the intrinsic mode function. As a result, it can help us to improve the decomposition’s accuracy. Finally, for a mixing mode, the energy of pseudo mode function is proposed on the cross-correlation at time zero, and it can be used to measure the ratio of signal mode to pseudo mode. Examples and experimental data are illustrated to prove the validity of the presented approach. The research results show that this approach can improve EMD performance while a pseudo mode occurs in the decomposition.

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

2014 ◽  
Vol 229 (9) ◽  
pp. 1630-1638 ◽  
Author(s):  
Jian-hua Cai
Keyword(s):  
Power Spectrum ◽  
Order Statistics ◽  
Empirical Mode Decomposition ◽  
Gaussian Noise ◽  
Rolling Bearing ◽  
Higher Order ◽  
Intrinsic Mode Function ◽  
Order Spectrum ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

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