Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT

In power distributed generation system, analysis of power quality is a major issue and hence it is essential to implement an efficient power quality system. To address and end this issue, the proposed work is mainly focused on the detection and classification of power quality disturbances or events by applying Hilbert–Huang transform (HHT) technique. HHT technique is a novel signal processing algorithm that comprises two processes. First is the empirical mode decomposition (EMD), which is an iterative process where N number of power quality signals are decomposed into intrinsic mode functions (IMFs). The second process, Hilbert transform, is applied to each IMF component. The advantage of using these two processes makes it an attractive power tool for quality event analysis. Once the IMF is obtained, it is easy to construct a time–frequency representation model. Then, the significant features are extracted from amplitude, phase and frequency instantly, which are the contour of IMFs of each power quality disturbance. For classification, adaptive neuro-fuzzy system (NFS) classifier is used. It is an intelligent system used for the combination of neural network and fuzzy logic. Power quality features are given as input to the NFS system. The experimental simulation is conducted in a MATLAB environment, and proposed HHT has higher efficiency compared to existing methods.

Fault classification in power systems using EMD and SVM

Electrocardiogram (ECG) is a measure of an electrical activity of heart which is used to analyze the functioning of the heart of a person. This analysis helps the physician to diagnose the condition of the patient. As the ECG signals are non-stationary in nature, they cannot be analyzed with earlier techniques as they are limited. We therefore need robust methods like Hilbert-Huang Transform and Wavelet Transform to analyze such signals. In this paper I have employed Hilbert Huang Transform to analyze the ECG signal and plotted the time-frequency plot. HHT is a latest data analysis method proposed by Huang et al. which analyses non-linear and non-stationary signals by decomposing them into Intrinsic Mode Functions (IMF) followed by finding out their instantaneous frequencies of Intrinsic Mode Functions (IMF) with Hilbert Transform.

Scramjet combustor states parameters dynamic characteristics study have great importance in scramjet accuracy thermodynamics regulations. In this investigation the dynamic response characteristics of kerosene-fueled scramjet combustor wall pressures are studied by time-frequency domain analysis method. Firstly, the dynamic response of scramjet combustor wall pressure is stimulated by step input of kerosene under different fuel equivalence ratio by using the direct-connected test facility. Secondly, the time-frequency spectrum of the dynamic response data are analyzed by using Hilbert-Huang transform (HHT) method. In HHT analysis, the raw experiment response data are filtered and decomposed into intrinsic mode functions (IMF) in time domain by the Empirical mode decomposition (EMD), then Hilbert spectral analysis (HSA) examine each IMF's instantaneous frequency as functions of time. Finally, time-frequency domain character distributions with different fuel equivalence ratio(ER) and wall pressure measurement location are given and analyzed by comparison study.

Because Raman peaks of the biological sample are superimposed on each other, the use of characteristic peak attribution is limited to some extent. In this study, we show that Hilbert–Huang transform (HHT) provides a Raman spectral feature extracting method, especially for biological samples. First, the empirical mode decomposition algorithm was used to decompose Raman spectra into intrinsic mode functions (IMFs). It is worth noticing that the IMF frequency is single or nearly single, so its further transformation (instantaneous amplitude, instantaneous angle, instantaneous angular frequency, Hilbert spectrum, and Hilbert marginal spectrum) from Hilbert processing is monotonous instead of the raw overlapping. Then, the Hilbert marginal spectrum was selected by one‐way analysis of variance and related with the rice type to establish a partial least squares regression (PLS) model with a 95.00% accuracy. This result is better than those based on characteristic variables screened by PLS, interval PLS, principal component analysis, independent component analysis, successive projections algorithm, haar, db, and coif (85.00%, 90.00%, 82.50%, 77.50%, 90.00%, 92.50%, 80.00%, and 85.00%). These results illustrate that HHT can accurately extract the characteristic variables from biological Raman spectra. The classification accuracy based on HHT is slightly lower than those based on bior 2.4, three‐layer decomposition (97.50%) and sym 5, five‐layer decomposition (97.50%). Significantly, no parameters need to be set such as the wavelet mother function and the decomposition layer in the HHT feature extraction process. This paper provides a HHT method for Raman spectral feature extraction, which is simple and effective.

This paper proposes an identification method for general linear time‐varying (LTV) MDOF systems and weakly nonlinear systems based on the Hilbert‐Huang Transform (HHT)[1]. The proposed method uses Empirical mode decomposition (EMD) to decompose the response signals of systems into intrinsic mode functions (IMFs) and residues, and then analyzes the IMFs and the residues by Hilbert transform (HT) to obtain the analytical IMFs and analytical residues. After that, the above signals are synthesized to form new response signals and new analytical response signals. Finally, the new synthesized signals are used to identify the stiffness and damping coefficients of the systems. Three types of variation: smooth, abrupt and periodical variations are considered in the numerical simulations of LTV chainlike[2] and nonchainlike systems as well as weakly nonlinear systems such as Duffing oscillators and Van der Pol oscillators with white noise added in the system responses to demonstrate the effectiveness, accuracy and robustness of the proposed method. (© 2011 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

A novel denoising method based on empirical mode decomposition (EMD) is put forward in this paper. With the new method, the nonlinear and non-stationary signal is decomposed into intrinsic mode functions (IMF) via EMD, then, high frequency IMFs will be processed by threshold denoising approach. The difference between the proposed method and the wavelet transform (WT) is that the EMD method can be applied to nonlinear processes and non-stationary processes adaptively. With the new method, there is no need selecting the base wavelet and it can be applied easily to analyze signals. In order to solve the problem of end effect in EMD, an approach of data extending based on minimum mean-square deviation approach is adopted in the present study. As the result of using the real MEMS signal for experiment the standard deviation was reduced to 0.027 times the original one in new ways. By analyzing the Allan variance, types of noises existing in the signal of MEMS gyro are restrained.

The analysis of electroencephalogram (EEG) signals is becoming more important because of the time-consuming and large bias of traditional visual detection technology, especially in diagnosis of epilepsy. Based on the nonlinear and non-stationary of the EEGs, empirical mode decomposition (EMD) is applied to decompose the original signals into intrinsic mode functions (IMFs). In this paper, after getting the IMFs, the fluctuation index and variation coefficient are calculated to analyze the amplitude change of IMFs. In order to better reflect the information of EEG signals, Hilbert transform is applied to obtain the instantaneous frequency for each IMF. Then, the novel feature named fluctuation index and variation coefficient of instantaneous frequency for IMFs are calculated. Furthermore, feature based on sample entropy of the first order difference is extracted. Finally, both of the calculated features will put together as a fusion feature into SVM for classification. The proposed method is evaluated using the Boon epileptic dataset and the highest average classification accuracy is 99.59%, showing a powerful method to detect seizure.

Hilbert-Huang Transform (HHT) is a kind of signal analysis method suitable for non-stationary signal, and the combination of HHT and vector signal processing is called vector HHT, by which the instantaneous azimuth of the target can be estimated. In vector HHT signal processing method, the existence of intermittent signal and noise leads to mode mixing in the results of empirical mode decomposition (EMD). Mode mixing influences exact extraction of vibration mode, which causes that the signal which should be in the same order of intrinsic mode function (IMF) distributes in different IMFs. In vector HHT signal processing method, the two vibration speed components need to be multiplied by sound pressure respectively, so mode mixing will lead to great error in the estimation of azimuth of the target. To overcome the influence of mode mixing on vector HHT, this paper proposes a kind of vector HHT signal processing method based on scale filtering. The new method can effectively eliminate mode mixing resulting from intermittent signal and noise, and exact decomposition results can be achieved. The improved vector HHT method is used in processing acoustic vector signal, and the data obtained from a certain sea experiment in Dalian city is analyzed by the new method. The waterfall plot accurately shows the azimuth of the target ship, which proves the validity of the method.

Empirical mode decomposition (EMD) is an adaptive method for decomposing a signal into intrinsic mode functions (IMFs).This study explored using EMD of ultrasound imaging for gain-independent measurements on tissue echogenicity. The IMF-based echogenicity ratio (IER) was proposed using the first (C1) and second IMFs (C2) of ultrasound radiofrequency data. Experiments on lipid phantoms were conducted to investigate the practical performance of IER. Phantoms with lipid concentrations 0%–30% (n = 36) were scanned using a clinical ultrasound scanner to acquire the radiofrequency data under different gains (12–33 dB) for EMD and IER calculations. Experiments on a tissue-mimicking phantom were further performed using the same ultrasound system and data acquisition procedure to investigate the effect of ultrasound frequency on the IER at5–8 MHz.Experimental results showed that the IER measured under 33-dB gain decreased from 6.65 ± 0.23 to 3.97 ± 0.10 when the lipid concentrations were increased from 0% to 30%. When 12-dB gain was used, the IER decreased from 6.21 ± 0.29 to 3.39 ± 0.07. However, whenincreasing the frequency, the IER had a mean decreasing rate of −8.67% per MHz, which was lower than those of the C1 and C2 intensities.The proposed IER may allow gain-independent measurement on tissue echogenicity.

Identifying flight modes of Aerial Planting Projectile using Hilbert-Huang transformation

In order to improve the accuracy of rail crack detection, especially for the high-speed condition, this paper proposes a rail crack detection method based on the adaptive noise cancellation method of empirical mode decomposition (EMD). The acoustic emission (AE) signals of simulated crack signals are obtained by the rail-wheel test rig, and these signals are decomposed into intrinsic mode functions (IMFs) by EMD. The cross-correlation analysis is used to reserve the IMFs which contains useful information. Then, the denoising signals are reconstructed based on the reserved IMFs, and wavelet transform is employed to detect the crack signals from the denoising signals. Signals at different speeds are investigated by the proposed method and the interference of noise signals is suppressed effectively. The results illustrate that the proposed method can detect the rail crack signals effectively at high speed with noise.

The empirical mode decomposition (EMD) method is a technique that recursively decomposes an input signal into intrinsic mode functions (IMFs) by residual signals, primarily for identifying desirable features. The suggested algorithm observes the residual signal instead of the IMF, which lowers the computing load. The study introduces a new method for detecting bearing faults by enhancing signal extraction from sensor data using EMD and multi-axis feature extraction. This method streamlines the process by filtering out high-frequency noise and correlating residual signal information with analysis. The approach also enhances the signal-to-noise ratio (SNR) and feature signature identification using digital signal processing (DSP) techniques. The algorithm for vibration data analysis is tested for bearing failures, identifying shaft frequency and inner race bearing faults, which can be implemented in parallel. For the inner race fault bearing analysis, two-level EMD with a residual signal generates output similar to five-iteration EMD, saving 60% of computations. The use of spectral multiplication to multi-axis data processing produced a rise in the SNR of 18.32 dB to 20.92 dB for Y-axis and X-axis input, respectively. When compared to the single-axis IMF data computation, 20% fewer iterations are needed overall. A single-level EMD is adequate for calculating the rotational frequency of a healthy bearing. For the Y- and X-axis input, multi-axis analysis increases SNR by 10.68 dB and 13.14 dB, accordingly. This comprehensive strategy reduces computational complexity, improves fault detection accuracy, and minimizes noise impact, making it a promising solution for bearing fault detection.

A brainwave classification, which does not involve any limb movement and stimulus for character-writing applications, benefits impaired people, in terms of practical communication, because it allows users to command a device/computer directly via electroencephalogram signals. In this paper, we propose a new framework based on Empirical Mode Decomposition (EMD) features along with the Gaussian Mixture Model (GMM) and Kernel Extreme Learning Machine (KELM)-based classifiers. For this purpose, firstly, we introduce EMD to decompose EEG signals into Intrinsic Mode Functions (IMFs), which actually are used as the input features of the brainwave classification for the character-writing application. We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification, in terms of character applications, because of the wavelet-like decomposition without any down sampling process. Secondly, by getting motivated with shallow learning classifiers, we can provide promising performance for the classification of binary classes, GMM and KELM, which are applied for the learning of features along with the brainwave classification. Lastly, we propose a new method by combining GMM and KELM to fuse the merits of different classifiers. Moreover, the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement. The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform (DWT) feature. Moreover, we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40% and 80.10%, respectively, which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%. Furthermore, we obtained the improved performance by combining GMM and KELM, i.e., average accuracy of 80.60%. These outcomes exhibit the usefulness of the EMD feature combining with GMM and KELM based classifiers for the brainwave classification in terms of the Character-Writing application, which do not require any limb movement and stimulus.

Exhaust gas temperature is one of the important performance characterization parameters of the state of engine. The prediction analysis of the Exhaust Gas Temperature Margin (EGTM) series is helpful to estimate engine’s performance, which can offer theory support for the fault detection and diagnose. Aiming at the non-linear and non-stationary features of EGTM data, a prediction method based on Empirical Mode Decomposition (EMD) and Least Squares Support Vector Regression (LS-SVR) is proposed. The EMD method is used to decompose the EGTM data to reduce the complexity of the time series. The EGTM data were decomposed into Intrinsic Mode Function (IMF) and the residual series by EMD. Finally, the prediction model were build the different LS-SVR for predicting each IMF and the residual series. Each prediction results of the series were combined to obtain EGTM forecast results. The results show that compared with the traditional prediction method, RMSE and MAE are reduced to 2.3178 and 1.8388, which improves the prediction accuracy effectively.