The Applied Research of the Hilbert-Huang Transform and Wavelet Transform in the Fault Location of Transmission Line

2013 ◽  
Vol 291-294 ◽  
pp. 2432-2436
Author(s):  
Zhi Bin Li ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Wavelet Transform ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Decomposition Processes ◽  
Before And After ◽  
Better Than

In the process of the Hilbert-Huang transform, empirical mode decomposition (EMD) may result in the end effect and modal aliasing when processing data, so proposing Ensemble Empirical Mode Decomposition (EEMD) instead of EMD, and assessing the accuracy of the two decomposition processes according to the total energy of the signal before and after the decomposition. Take a comparison between the Hilbert-Huang transform and the wavelet transform, the localization showed that the Hilbert-Huang transform is better than wavelet transform in the fault location of transmission line.

Transmission Line Fault Location Based on Hilbert-Huang Transformation and SVM

2013 ◽  
Vol 333-335 ◽  
pp. 1673-1678
Author(s):  
Ke Qin Bao ◽  
Bao Xing Wu ◽  
Yun Hui Xu
Keyword(s):  
Support Vector Machine ◽  
Transmission Line ◽  
Empirical Mode Decomposition ◽  
Fault Location ◽  
Signal Sequence ◽  
Hilbert Transformation ◽  
Support Vector ◽  
End Effect ◽  
Positioning Accuracy ◽  
Mode Decomposition

In the process of the Hilbert-Huang Transformation, empirical mode decomposition (EMD) and Hilbert Transformation of the IMF components may result in the terminal effect, utilizing the support vector machine (SVM) extend the signal sequence and IMF components to weaken the end effect. The paper analyzes the fault signal which extracted under the different fault conditions to complete the fault location. The simulation result shows that using SVM can effectively restrain terminal effect; In the different fault states can have a high positioning accuracy.

Application of Hilbert–Huang Transform With Improved Ensemble Empirical Mode Decomposition in Nonlinear Flight Dynamic Mode Characteristics Estimation

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
S. Abolfazl. Mokhtari ◽  
Mehdi. Sabzehparvar
Keyword(s):  
Empirical Mode Decomposition ◽  
Test Data ◽  
Flight Test ◽  
Ensemble Empirical Mode Decomposition ◽  
Dynamic Mode ◽  
Original Signal ◽  
Complex Signals ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Highly Nonlinear

Identification of aircraft flight dynamic modes has been implemented by adopting highly nonlinear flight test data. This paper presents a new algorithm for identification of the flight dynamic modes based on Hilbert–Huang transform (HHT) due to its superior potential capabilities in nonlinear and nonstationary signal analysis. Empirical mode decomposition and ensemble empirical mode decomposition (EEMD) are the two common methods that apply the HHT transform for decomposition of the complex signals into instantaneous mode frequencies; however, experimentally, the EMD faces the problem of “mode mixing,” and EEMD faces with the signal precise reconstruction, which leads to imprecise results in the estimation of flight dynamic modes. In order to overcome (handle) this deficiency, an improved EEMD (IEEMD) algorithm for processing of the complex signals that originate from flight data record was introduced. This algorithm disturbing the original signal using white Gaussian noise, IEEMD, is capable of making a precise reconstruction of the original signal. The second improvement is that IEEMD performs signal decomposition with fewer number of iterations and less complexity order rather than EEMD. This algorithm has been applied to aircraft spin maneuvers flight test data. The results show that implication of IEEMD algorithm on the test data obtained more precise signal extractions with fewer iterations in comparison to EEMD method. The signal is reconstructed by summing the flight modes with more accuracy respect to the EEMD. The IEEMD requires a smaller ensemble size, which results in saving of a significant computational cost.

scholarly journals Gearbox Composite Fault Diagnosis Method Based on Minimum Entropy Deconvolution and Improved Dual-Tree Complex Wavelet Transform

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 18 ◽  
Author(s):  
Ziying Zhang ◽  
Xi Zhang ◽  
Panpan Zhang ◽  
Fengbiao Wu ◽  
Xuehui Li
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Notch Filter ◽  
Vibration Signal ◽  
Coefficient Matrix ◽  
Ensemble Empirical Mode Decomposition ◽  
Minimum Entropy ◽  
Complex Wavelet Transform ◽  
Mode Decomposition ◽  
Complex Wavelet

Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

scholarly journals Detection of stretch reflex onset based on empirical mode decomposition and modified sample entropy

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Mingjia Du ◽  
Baohua Hu ◽  
Feiyun Xiao ◽  
Ming Wu ◽  
Zongjun Zhu ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Stretch Reflex ◽  
Recognition Rate ◽  
Sample Entropy ◽  
Decomposition Algorithm ◽  
Surface Electromyogram ◽  
Mode Decomposition ◽  
Before And After ◽  
Surface Electromyogram Signal ◽  
Better Than

Abstract Background Accurate spasticity assessment provides an objective evaluation index for the rehabilitation treatment of patients with spasticity, and the key is detecting stretch reflex onset. The surface electromyogram of patients with spasticity is prone to false peaks, and its data length is unstable. These conditions decrease signal differences before and after stretch reflex onset. Therefore, a method for detecting stretch reflex onset based on empirical mode decomposition denoising and modified sample entropy recognition is proposed in this study. Results The empirical mode decomposition algorithm is better than the wavelet threshold algorithm in denoising surface electromyogram signal. Without adding Gaussian white noise to the electromyogram signal, the stretch reflex onset recognition rate of the electromyogram signal before and after empirical mode decomposition denoising was increased by 56%. In particular, the recognition rate of stretch reflex onset under the optimal parameter of the modified sample entropy can reach up to 100% and the average recognition rate is 93%. Conclusions The empirical mode decomposition algorithm can eliminate the baseline activity of the surface electromyogram signal before stretch reflex onset and effectively remove noise from the signal. The identification of stretch reflex onset using combined empirical mode decomposition and modified sample entropy is better than that via modified sample entropy alone, and stretch reflex onset can be accurately determined.

Empirical Mode Decomposition and Robust Pitch Detection Based on Recurrence Analysis

2013 ◽  
Vol 303-306 ◽  
pp. 1035-1038
Author(s):  
Jing Fang Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Pitch Detection ◽  
Intrinsic Mode Functions ◽  
Noisy Environment ◽  
Method Performance ◽  
Recurrence Analysis ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Band Filter ◽  
Better Than

A new pitch detection method is designed by the recurrence analysis in this paper, which is combined of Empirical Mode Decomposition (EMD) and Elliptic Filter (EF). The Empirical Mode Decomposition (EMD) of Hilbert-Huang Transform (HHT) are utilized tosolve the problem, and a noisy voice is first filtered on the elliptic band filter. The two Intrinsic Mode Functions (IMF) are synthesized by EMD with maximum correlation of voice, and then the pitch be easily divided. The results show that the new method performance is better than the conventional autocorrelation algorithm and cepstrum method, especially in the part that the surd and the sonant are not evident, and get a high robustness in noisy environment.

The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

2013 ◽  
Vol 300-301 ◽  
pp. 344-350 ◽  
Author(s):  
Zhou Wan ◽  
Xing Zhi Liao ◽  
Xin Xiong ◽  
Jin Chuan Han
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Hilbert Huang Transform ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Eemd Method

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

scholarly journals Study on Icing Prediction of Power Transmission Lines Based on Ensemble Empirical Mode Decomposition and Feature Selection Optimized Extreme Learning Machine

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2163 ◽  
Author(s):  
Weijun Wang ◽  
Dan Zhao ◽  
Liguo Fan ◽  
Yulong Jia
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Transmission Line ◽  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Transmission Lines ◽  
Ensemble Empirical Mode Decomposition ◽  
Ice Thickness ◽  
Mode Decomposition ◽  
Learning Machine

The ice coating on the transmission line is extremely destructive to the safe operation of the power grid. Under natural conditions, the thickness of ice coating on the transmission line shows a nonlinear growth trend and many influencing factors increase the difficulty of forecasting. Therefore, a hybrid model was proposed in this paper, which mixed Ensemble Empirical Mode Decomposition (EEMD), Random Forest (RF) and Chaotic Grey Wolf Optimization-Extreme Learning Machine (CGWO-ELM) algorithms to predict short-term ice thickness. Firstly, the Ensemble Profit Mode Decomposition model was introduced to decompose the original ice thickness data into components representing different wave characteristics and to eliminate irregular components. In order to verify the accuracy of the model, two transmission lines in ‘hunan’ province were selected for case study. Then the reserved components were modeled one by one, building the random forest feature selection algorithm and Partial Autocorrelation Function (PACF) to extract the feature input of the model. At last, a component prediction model of ice thickness based on feature selection and CGWO-ELM was established for prediction. Simulation results show that the model proposed in this paper not only has good prediction performance, but also can greatly improve the accuracy of ice thickness prediction by selecting input terminal according to RF characteristics.

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