Adaptive Ensemble Empirical Mode Decomposition Research Articles

Partial Discharge Signal Denoising Algorithm Based on Aquila Optimizer–Variational Mode Decomposition and K-Singular Value Decomposition

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance.

Extraction of periodic signals in Global Navigation Satellite System (GNSS) vertical coordinate time series using the adaptive ensemble empirical modal decomposition method

Abstract. Empirical modal decomposition (EMD) is an efficient tool for extracting a signal from stationary or non-stationary time series and is enhanced in stability and robustness by ensemble empirical mode decomposition (EEMD). Adaptive EEMD further improves computational efficiency through adaptability in the white noise amplitude and set average number. However, its effectiveness in the periodic signal extraction in Global Navigation Satellite System (GNSS) coordinate time series regarding the inevitable missing data and offset issues has not been comprehensively validated. In order to thoroughly investigate their impacts, we simulated 5 years of daily time series data with different missing data percentages or a different number of offsets and conducted them 300 times for each simulation. The results show that high accuracy could reach the overall random missing rate below 15 % and avoid consecutive misses exceeding 30 d. Meanwhile, offsets should be corrected in advance regardless of their magnitudes. The analysis of the vertical components of 13 stations within the Australian Global Sea Level Observing System (GLOSS) monitoring network demonstrates the advantage of adaptive EEMD in revealing the time-varying characteristics of periodic signals. From the perspectives of correlation coefficients (CCs), root mean square error (RMSE), power spectral density indices (κ) and signal-to-noise ratio (SNR), the means for adaptive EEMD are 0.36, 0.81, −0.18 and 0.48, respectively, while for least squares (LS), they are 0.27, 0.86, −0.50 and 0.23. Meanwhile, a significance test of the residuals further substantiates the effectiveness in periodic signal extraction, which shows that there is no annual signal remaining. Also, the longer the series, the higher the accuracy of the reasonable extracted periodic signal concluded via the significance test. Moreover, driving factors are more effectively facilitated by the time-varying periodic characteristics compared with the constant periodic signal derived by LS. Overall, the application of adaptive EEMD could achieve high accuracy in analyzing GNSS time series, but it should be based on properly dealing with missing data and offsets.

Non-Contact Detection of Vital Signs Based on Improved Adaptive EEMD Algorithm (July 2022).

Non-contact vital sign detection technology has brought a more comfortable experience to the detection process of human respiratory and heartbeat signals. Ensemble empirical mode decomposition (EEMD) is a noise-assisted adaptive data analysis method which can be used to decompose the echo data of frequency modulated continuous wave (FMCW) radar and extract the heartbeat and respiratory signals. The key of EEMD is to add Gaussian white noise into the signal to overcome the mode aliasing problem caused by original empirical mode decomposition (EMD). Based on the characteristics of clutter and noise distribution in public places, this paper proposed a static clutter filtering method for eliminating ambient clutter and an improved EEMD method based on stable alpha noise distribution. The symmetrical alpha stable distribution is used to replace Gaussian distribution, and the improved EEMD is used for the separation of respiratory and heartbeat signals. The experimental results show that the static clutter filtering technology can effectively filter the surrounding static clutter and highlight the periodic moving targets. Within the detection range of 0.5 m~2.5 m, the improved EEMD method can better distinguish the heartbeat, respiration, and their harmonics, and accurately estimate the heart rate.

Design of sEMG Acquisition Circuit and Its Adaptive EEMD Denosing Research

The Surface electromyography (sEMG) signal is a kind of electrical signal which generated by human muscles during contraction. It is prone to being affected by noise because of its small amplitude, so it is necessary to remove the noise in its original signal with an appropriate algorithm. Based on the traditional signal denoising indicators, a new complex indicator r has been proposed in this paper which combines three different indicator parameters, that is, Signal to Noise Ratio (SNR), correlation coefficient (R), and standard error (SE). At the same time, an adaptive ensemble empirical mode decomposition (EEMD) method named AIO-EEMD which based on the proposed indicator is represented later. To verify the effective of the proposed algorithm, an electromyography signal acquisition circuit is designed firstly for collecting the original sEMG signal. Then, the denosing performance from the designed method is been compared with empirical mode decomposition (EMD) method and wavelet transform noise reduction method, respectively. The experiment results shown that the designed algorithm can not only automatically get the numbers of the reconstructed signal numbers, but also obtain the best reduction performance.

Long-term and seasonal variation in groundwater storage in the North China Plain based on GRACE

The over exploitation of groundwater has seriously restricted the healthy development of social economy in North China Plain (NCP). The study of the change in the groundwater storage at different time scales is helpful to understand the characteristics of groundwater dynamic changes and real-time groundwater management. Based on Gravity Recovery and Climate Experiment (GRACE) satellite products and Global Land Data Assimilation System (GLDAS) data, this paper analyses the seasonal and long-term trend changes of NCP groundwater storage from June 2003 to June 2017 by using adaptive Ensemble Empirical Mode Decomposition (EEMD) algorithm. Moreover, their relationships with precipitation and the South-to-North Water Diversion Middle Route Project (SWDP) are analysed. The main conclusions are as follows: (1) The change of groundwater storage in NCP mainly shows a long-term downward trend and the downward trend gradually increases from northeast to southwest, and the maximum decline rate appears in Handan, reaching 26.44 mm/yr; (2) The main spatial pattern in the seasonal variation of groundwater storage in the NCP has a good consistency and the response of seasonal groundwater variation to seasonal precipitation variation lags by 3–4 months; and (3) After implementing the SWDP, the long-term decline rate of groundwater storage is reduced and the average contribution rate of seasonal variation characteristics of groundwater storage increases from 20% to 28%. The research results of this paper are helpful to understand the dynamic change process of groundwater storage and lay a foundation for further exploring the reasons for the change of groundwater storage.

Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising

With the aid of ensemble empirical mode decomposition (EEMD), de-noising of the electrocardiogram (ECG) signal based on the genetic particle filter and fuzzy thresholding is proposed in this paper, which effectively eliminates noise from the ECG signal. This paper proposes a two-phase scheme for removing noise from ECG signal. In the first phase, noisy signal is decomposed into true intrinsic mode functions (IMFs) with the help of EEMD. Adaptive EEMD (AEEMD) is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise are obtained by using spectral flatness of each IMF and fuzzy thresholding. Corrupted IMFs are filtered using genetic particle filter to remove the noise. Finally, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for different databases and it gives better signal-to-noise ratio and root mean square error than other existing techniques.

Harmonic Detection Technology for Power Grids Based on Adaptive Ensemble Empirical Mode Decomposition

Harmonic detection and control for power grids have always been major concerns for researchers. With the application of diverse semiconductor materials in power systems, numerous asymmetrical loads arise, resulting in increasingly poor performance of traditional harmonic detection methods. Ensemble empirical mode decomposition (EEMD) provides a new approach for harmonic detection in power systems. Because the harmonic waves in power systems are indeterminate, optimal decomposition results cannot be achieved by means of artificially configured parameters. For such cases, the development of deep neural networks has provided a new solution for harmonic detection. In this study, particle swarm optimization is combined with a deep neural network to establish an adaptive harmonic separation algorithm. By training an adaptive model in this manner, adaptive EEMD can be realized. Moreover, decomposition parameters can be established based on the harmonic content of signals to effectively separate harmonic waves of diverse orders.

Processing of impedance cardiogram differential for non-invasive cardiac function detection

The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.

A Novel Adaptive EEMD Method for Switchgear Partial Discharge Signal Denoising

The elimination of a variety of noises such as the narrow-band interference in the detection of partial discharge (PD) signals in switchgear is an intractable issue. Furthermore, the self-adaptation in the denoising process is weak. A partial discharge-based novel adaptive ensemble empirical mode decomposition (Novel Adaptive EEMD, NAEEMD) method is proposed in this paper for noise reduction. First, the signal is decomposed using the EEMD, only the first-order natural mode is decomposed until the signal margin reaches the EEMD decomposed termination condition. After removing the first-order mode, noise is added to the residual signal, and the remaining signal components are decomposed in the next stage. At last, the intrinsic mode function (IMF) of the noise reduction reconstruction is adaptively selected. The latter is accomplished by combining the energy density and the average period of the IMF correlation coefficient method. Meanwhile, the proposed method provides a new strategy for pre-processing the PD signal of the switchgear. The outcomes of the proposed NAEEMD de-noising method have been compared with the conventional wavelet denoising algorithm (WDA) and EMD-based threshold denoising for validation. The simulation results showed a good denoising effect and effectiveness of the proposed method compared to the WDA and EMD-based threshold denoising. Furthermore, an experimental simulation utilizing actual switchgear PD signal has been performed to verify the noise reduction effectiveness of the proposed method.

The multi-timescale temporal patterns and dynamics of land surface temperature using Ensemble Empirical Mode Decomposition

Temporal variation patterns of Land Surface Temperature (LST) under different time scales are crucial in understanding the response of urban thermal environment to different forcings. However, there is no integrated toolset to extract such patterns from satellite remotely sensed time series LST (TSLST) data. This paper presents a workflow to extract the multi-timescale temporal patterns and dynamics from nonlinear and non-stationary TSLST data by taking Wuhan, China as case study. The 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products spanning the 2003–2017 period are used to generate a TSLST dataset with continuous and smooth surfaces on the monthly basis through the non-parametric Multi-Task Gaussian Process Modeling (MTGP). The study area is segmented into multiple time series clusters by k-means to bridge with urban planning in terms of research and implementation scale. Then, temporal patterns including annual, interannual components, and overall trends are reconstructed based on the components with characteristic time scales decomposed by the adaptive Ensemble Empirical Mode Decomposition (EEMD) method. The generated patterns of the 17 time series clusters are interpreted from the perspective of earth revolution, meteorological cycles and urbanization. Specifically, the annual components which are mainly generated by earth revolution reveal consistent rhythmic patterns among the time series. The interannual components preserve similar shapes although they differ in amplitudes. The overall shape is basically consistent with that of air temperature of Central China, which may be mainly induced by the El Niño-Southern Oscillation (ENSO) phenomenon. The overall trends which exert considerable differences are grouped into three types by shape. Such differences may be potentially caused by the inconsistent levels of localized urbanization, afforestation or circular economy development. This study facilitates the understanding of TSLST patterns and human-environment interactions. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.

Application of time–frequency entropy from wake oscillation to gas–liquid flow pattern identification

Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S(1–x)φ and (1–β)ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.

Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence-Based Variable Translation Wavelet Neural Network.

As a sound signal has the advantages of non-contacted measurement, compact structure, and low power consumption, it has resulted in much attention in many fields. In this paper, the sound signal of the coal mining shearer is analyzed to realize the accurate online cutting pattern identification and guarantee the safety quality of the working face. The original acoustic signal is first collected through an industrial microphone and decomposed by adaptive ensemble empirical mode decomposition (EEMD). A 13-dimensional set composed by the normalized energy of each level is extracted as the feature vector in the next step. Then, a swarm intelligence optimization algorithm inspired by bat foraging behavior is applied to determine key parameters of the traditional variable translation wavelet neural network (VTWNN). Moreover, a disturbance coefficient is introduced into the basic bat algorithm (BA) to overcome the disadvantage of easily falling into local extremum and limited exploration ability. The VTWNN optimized by the modified BA (VTWNN-MBA) is used as the cutting pattern recognizer. Finally, a simulation example, with an accuracy of 95.25%, and a series of comparisons are conducted to prove the effectiveness and superiority of the proposed method.

Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm

It becomes a key technology to measure the concentration of the vehicle exhaust components with the transmission spectra. But in the conventional methods for noise reduction and baseline correction, such as wavelet transform, derivative, interpolation, polynomial fitting, and so forth, the basic functions of these algorithms, the number of decomposition layers, and the way to reconstruct the signal have to be adjusted according to the characteristics of different components in the transmission spectra. The parameter settings of the algorithms above are not transcendental, so with them, it is difficult to achieve the best noise reduction effect for the vehicle exhaust spectra which are sharp and drastic in the waveform. In this paper, an adaptive ensemble empirical mode decomposition (EEMD) denoising model based on a special normalized index optimization is proposed and used in the spectral noise reduction of vehicle exhaust NOX. It is shown with the experimental results that the method can effectively improve the accuracy of the spectral noise reduction and simplify the denoising process and its operation difficulty.

The Adaptive EEMD Denoising Method based on Normalized Index Optimization for Near Infrared Spectra

Firstly, modes overlapping occurs easily between Near Infrared spectral high frequency signal and noise, so the Ensemble Empirical Mode Decomposition (EEMD) method was used to remove the noise. According to the approximate completeness of Near Infrared spectrum, an EEMD denoising method based on normalized index optimization. This method views the global optimal point of normalized denoising index as stop condition, and completes itself adaptively with no parameters setting during the decomposion. It can be concluded by Near Infrared spectrum denoising data that this method improves the denoising accuracy and decreases the difficulty in operation in an easy and efficient way.

Adaptive Ensemble Empirical Mode Decomposition and Its Application to Fault Detection of Planetary Gearboxes

Empirical mode decomposition(EMD) has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition(EEMD) is proposed accordingly. EEMD can reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithm. In current studies on EEMD, the parameters are generally selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed. In the method, the sifting number is adaptively selected and the amplitude of the added noise changes with the signal frequency during the decomposition process. Both simulations and a case of fault detection of a planetary gear demonstrate that the proposed method obtains the improved results compared with the original EEMD.

Fault Diagnosis of Rotating Machinery Based on an Adaptive Ensemble Empirical Mode Decomposition

The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery.