Papers by Keyword: Hilbert-Huang Transform (HHT)

Abstract: Based on Hilbert-Huang Transform (HHT) theory, we present a method to analyze the electroencephalogram (EEG) signal of right and left hand motor imagery. Firstly, EMD method decomposed EEG signal into a group of intrinsic mode functions (IMFs). The first three IMFs were extracted to denoise. We adopt endpoint Mirror Extension method to relieve the influence on subsequent processing brought by endpoint effect. According to the Hilbert transform, we can obtain the time-frequency distribution. The energy of the first three components is selected as the input of SVM. The results show that EMD is an efficient method to analyze the EEG signal. The proposed method obtains an ideal recognition rate.

Abstract: In this paper, the abnormal sound of bearing pad engine signal was studied. As the car engine signal is nonlinear and non-stationary, Hilbert-Huang transform method diagnosis the normal and bearing pad abnormal sound engine signal was used. Hilbert spectrum and time-frequency distribution 3-d map was got. Through these we knew normal engine signal frequency was 210 Hz, the abnormal sound of bearing pad engine signal frequency mainly concentrated in the 500 Hz, 1500 Hz and 2700 Hz. The results showed that the cause of abnormal sound was bearing pad wear. Sound signal was used in this experiment, it was easy to get, and the HHT analysis can separate cause abnormal sound of the high frequency component from the abnormal sound signal. From analysis of it, the abnormal sound reasons can find easily. It provides a new simple and effective method to the abnormal sound of bearing pad fault diagnosis.

Abstract: 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.

Abstract: In this paper, the gears fault signal in the engine was studied. Hilbert-Huang transform was applied for the gears fault signal analysis. From the experiment, the normal engine frequency of 240 Hz was got and the gears fault signal frequency concentrated in 2800 Hz. Through the study of intrinsic mode function and the Hilbert spectrum, improper meshing gears were the cause of this problem. The results showed that this method can effectively extract the fault feature and found out the cause of the problem. A new effective method is provided for the gears fault diagnosis.

Abstract: In this paper, we studied voice signal with Gaussian noise reduction. Based on signal analysis and reconstruction principle. Application of Hilbert-Huang transform(HHT), analyzed voice signal with Gaussian noise. And comprised with wavelet transform(WT), obtained the correlation coefficient between HHT noise reduction signal and excluding the noise signal was 0.8986, WT was 0.7889. The results showed that in the voice signal with Gaussian noise, compared HHT and wavelet analysis correlation analysis values, HHT noise reduction ability was 10% higher than WT. This paper provided a new analytic method to the voice signal noise reduction and enhanced the accuracy of it.

Abstract: 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.

Abstract: HHT is widely used to analyze nonlinear and non-stationary signals. But how to extend boundaries of signals in decomposition processes is a key problem of HHT. A new technique based on response surface method (RSM), which establishes the recursive relations between sample points of signals, is presented to deal with this difficult problem. Besides, the boundary extension problem arising from HHT can be described by mathematical least squares problem but traditional gradient algorithms may diverge when the Hessian matrix of the object function of the least squares problem is non-positive. It has been proved that the generalized inverse of the linear equations (derived from the linear least squares problem) by singular value decomposition is the solution of original linear least squares problems. Thereby the divergence problem is also solved. Analysis results with respect to simulation signals and measured signals show that the method with new boundary extension technique performs successfully for HHT.

Abstract: Aiming at the problem that harmonic pollution is becoming more and more serious in power system, a new method to detect harmonics and inter-harmonics based on Hilbert marginal spectrum is proposed in this paper. Firstly, the original signal is decomposed into several Intrinsic Mode Functions through Empirical Mode Decomposition. Then Hilbert marginal spectrum is obtained through Hilbert Huang Transform. It contains the information of signal’s harmonics frequency and those amplitudes. Finally, both harmonics and inter-harmonics are detected by this method. Fourier transform lacks the ability of time-frequency analysis. Wavelet transform is affected by the selection of wavelet base. This method overcomes these shortages and can detect the component of each harmonic quickly and accurately. Simulation result verifies that this method can meet the requirement of voltage and current distortion detection in power system.

Abstract: Sympathetic inrush may cause the mal-operation of differential protection. Aiming at this problem, based on the characteristic that sympathetic inrush contains a lot of aperiodic component and high harmonics, this paper proposes to use Hilbert Huang transform to identify sympathetic inrush. Firstly, the mathematical model of sympathetic inrush is established to analyze its characteristic. Then Hilbert transform is used to obtain harmonic contents of sympathetic inrush and internal fault current. According to the proportion of fundamental to current, sympathetic current can be identified and differential protection can be blocked. The simulation results show that this method has better reliability and sensitivity than the second harmonic restraint scheme. Besides, it has a good ability against TA saturation.

Abstract: Vibration features of moving targets can reflect their detailed characteristics, which have important military and civil values. Coherent lidar is the preponderant method of target detection, because of its advantages such as high measurement accuracy and ability of long-distant non-destructive measurement, which is appropriate for detecting the vibration information of the target through remote sensing. Traditional analysis of moving target’s vibration always contained only one motion mode, which could not reflect the real complex motion of the target. This paper proposed a novel model of complex moving target’s vibration for coherent laser remote sensing detection. Considering the vibration signal is non-linear and non-stationary, Hilbert-Huang transform (HHT) was applied to the decomposition of the signal. After decomposition, the energy of the vibrating signal in some inherent frequency band was selected as the feature of the signal. Simulations and experiments were carried out to verify the divisibility of the feature, which could support the identification of target vibration feature based on coherent laser remote sensing detection.