Islanding Detection in DC Microgrids Based on Empirical Mode Decomposition

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

A Hilbert–Huang transform (HHT) is a time–frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD.

Application of the EEMD method for distinction and suppression of motion-induced noise in grounded electrical source airborne TEM system

Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS decomposition based on Ensemble Empirical Mode Decomposition (EEMD) is proposed. EEMD is achieved by sifting an ensemble of adaptive orthogonal components called Intrinsic Mode Functions (IMFs). EEMD is noise-assisted and overcomes the drawback of mode mixing in conventional Empirical Mode Decomposition (EMD). Inspired by these advantages, the aim of this work is to employ EEMD to decompose SITS into IMFs and to choose relevant IMFs for the separation of seasonal and trend components. In a series of simulations, IMFs extracted by EEMD achieved a clear representation with physical meaning. The experimental results of 16-day compositions of Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), and Global Environment Monitoring Index (GEMI) time series with disturbance illustrated the effectiveness and stability of the proposed approach to monitoring tasks, such as applications for the detection of abrupt changes.

<div class="section abstract"><div class="htmlview paragraph">Knock threshold detection is the key of closed loop control of ignition in gasoline engine, and it is also the difficult point in knock measurement. In this paper, an investigation of knock detection in turbocharged gasoline engine using bispectrum slice and ensemble empirical mode decomposition (EEMD) based on the engine cylinder head vibration signals is presented. By adding some finite amplitude Gaussian white noises to the signal, EEMD keeps the signal continuous in different time span, and therefore the mode mixing inhering in the classical empirical mode decomposition (EMD) method is alleviated. Power spectrum density (PSD) estimation is used to determine the band range of the resonance frequency generated by knock component. EEMD is used to decompose the original signals, the time-frequency characteristics of the Intrinsic Mode Functions (IMF) are analyzed using Continues Wavelet Transform (CWT) due to its excellent time-frequency resolution. Then bispectrum as a post processing method is adopted to process IMF which contains knock feature information. The diagonal slice of bispectrum is computed to extract the non-linear feature deriving from the quadratic phase coupling, as well as the knock characteristic frequencies. This method is endowed with characteristics of avoiding model mixing, suppressing Gaussian white noise, detecting the nonlinear coupling feature. And knock characteristic information, especially for light knock detonation can be successfully extracted. Derived from the above method, a novel knock intensity factor has been developed and the relevance of the proposed criterion for characterizing different levels of knock has been investigated.</div></div>

Application of the spectral analysis technique Ensemble Empirical Mode Decomposition (EEMD) to hydroclimatic time series produces an orthogonal set of decreasing frequency Intrinsic Mode Functions (IMFs) and a residual (or trend). As the EEMD algorithm is locally adaptive it is suitable for decomposing time series that appear non-stationary in terms of mean and variance: characteristics that are not uncommon in hydroclimatic series. EEMD utilises ensemble averaging to overcome the mode mixing problem observed in traditional Empirical Mode Decomposition (EMD). Here EEMD is briefly outlined and issues relating to the calculation of the Hilbert spectrum from EEMD output are discussed. EEMD is then applied to three climatic indices relevant to Australian hydroclimatic variability, namely the Southern Oscillation Index, Indian Ocean Dipole mode index and the Tasman Sea Index. Analysis of these indices reveals EEMD is capable of identifying physical process signals. For example, the known spectral structure of the SOI is identified by the EEMD algorithm. It was observed during analysis of the Indian Ocean Dipole mode index that EMD post processing may introduce end effects previously minimised by the EEMD ensemble average. These effects may inflate existing features, or potentially introduce low frequency artefacts, though this danger can be mitigated through careful examination of the complete Hilbert spectrum. EEMD and Hilbert spectrum analysis of hydroclimatic data provides insight into long-term trends, dominant frequencies and changes in energy with time for time series that are usually problematic to analyse with traditional spectral analysis techniques. The spectral fingerprints of the climatic indices assessed here are used in a companion paper that looks for those fingerprints within Australian precipitation and temperature data (Srikanthan et al., 2011).

Emotion feature extraction is the key to speech emotional recognition. And ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating emotion mode mixing present in the original empirical mode decomposition(EMD). To evaluate the performance of this new method, this paper investigates the effect of a parameters pertinent to EEMD: speech emotional envelope. Firstly, a speaker emotional envelope features extraction based on EEMD is proposed in the paper. Using the piecewise power function in speech emotional envelope has a better effect in emotional identification. At the same time, the proposed technique has been utilized for classification of fo ur kinds of emotional(angry, happy, sad and neutral) speech signals. Emotional intrinsic mode functions(IMFe) are obtained by empirical mode decomposition on emotional speech signals, the fast fourier transform(FFT) of each intrinsic mode function is extracte d as the emotional feature coefficient which is used in speaker emotional identification applying by vector quantization. MATLAB is used to ca lculate the characteristic of emotional speech signals using empirical mode decomposition (EEMD). We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop feature vectors. The results indicate the proposed method works well in speaker emotional identification.

A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition Methods: Application to bearing defect diagnosis

Background: Single-channel observed signal analysis based on independent component analysis (ICA) model belongs to the extremely underdetermined blind source separation (BBS) problem. Method: In order to extract the fault feature hidden in the single-channel measured signal from multi-stage gearbox, a joint approach of fault feature extraction based on ensemble empirical mode decomposition (EEMD) and constrained independent component analysis (CICA) is proposed. The single-channel vibration fault signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, which can overcome the shortcomings of classical empirical mode decomposition (EMD). By computing the kurtosis and correlation coefficients of each IMF, we can select some suitable IMFs to construct a newly observed vector combined with the original signal, which meets the requirement of CICA algorithm. Finally, the suitable reference signal including faulty gear meshing frequency is generated, and the desired gear low frequency slight feature is extracted by CICA combined with envelope analysis. Conclusion: Through the experiment analysis of fault feature extraction with a missing tooth on the low speed gear pairs, the effectiveness and applicability of the proposed method are verified. Keywords: Gearbox, single-channel measured signal, ensemble empirical mode decomposition (EEMD), constrained independent component analysis (CICA), fault feature extraction, FFT spectrum.

Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this paper, an improved sifting stop criterion based on the valid data segment is proposed, and is compared with the traditional one. Results show that the new sifting stop criterion avoids the influence of end effects and improves the correctness of the EMD. In addition, a novel AEMD method combining the analysis mode decomposition (AMD) and EMD is developed to solve the mode-mixing problem, in which EMD is firstly applied to dispose the original signal, and then AMD is used to decompose these mixed modes. Then, these decomposed modes are reconstituted according to a certain principle. These reconstituted components showed mode mixing phenomena alleviated. Model comparison was conducted between the proposed method with the ensemble empirical mode decomposition (EEMD), which is the mainstream method improved based on EMD. Results indicated that the AEMD and EEMD can effectively restrain the mode mixing, but the AEMD has a shorter execution time than that of EEMD.

Recently, ensemble empirical mode decomposition (EEMD) method has been developed for non-linear and non-stationary signal analysis. The method can work on nature signals (non-linear and nonstationary signals) and reduce the speckle noise. With the EEMD method, the signal is decomposed into several intrinsic mode functions (IMFs) and the frequencies of IMFs are arranged in decrease order (high to low) after the EEMD processing. The scaling mode of the EEMD method is similar to wavelet transform, but the signal resolutions in different frequency domains do not decrease by down-sampling. There are a large population and a developed economy in Weihe watershed, the disasters of droughts and floods caused by the autumn precipitation (here is precipitation in September and October) less or more than normal cause great loss and serious influence. In this paper, we propose the EEMD method to decompose the autumn precipitation series in the Weihe river basin during last 50 years into several IMFs, then extract the information including in the precipitation series and get the characteristics of multi-scales. The result shows that it is well response to the autumn precipitation series in the Weihe river basin and to the abrupt climate change in late 1970s and early 1980s of last century. The response appears earlier for high time scales than for low time scales In addition, the expression of the response for high time scales is the form of variability, but it is the amplitude of variability for low time scales.

Complex nonstationary and nonlinear signals can be adaptively analyzed by the Hilbert-Huang transform(HHT) through empirical mode decomposition(EMD) and the Hilbert transform to generate the Hilbert-Huang spectrum.The Hilbert-Huang spectrum is able to show the local characteristics of the signals and focus time-frequency information well,and therefore it is widely applied to analyzing vibration signals in the field of machinery fault diagnosis.EMD,however,sometimes cannot reveal the signal characteristics accurately because of the problem of mode confusion.Moreover,the extracted intrinsic mode functions(IMFs) have different sensitivity to faults.Some IMFs are sensitive and closely related to the faults but others are irrelevant.Thus it is necessary to enhance the fault diagnosis accuracy of the HHT based on all IMFs of EMD.Aiming at this problem,an improved HHT based on ensemble empirical mode decomposition(EEMD) and sensitive IMFs is proposed,in which EEMD is used to alleviate the problem of mode confusion,and the sensitivity evaluation algorithm to select sensitive IMFs and highlight potential fault characteristics.Thus,the fault diagnosis results obtained by the proposed method are more accurate than the HHT based on all IMFs of EMD.Both simulated signals and real signals of an early rub-impact fault verify the effectiveness of the improved HHT.

Random and coherent noise exists in microseismic and seismic data, and suppressing noise is a crucial step in seismic processing. We have developed a novel seismic denoising method, based on ensemble empirical mode decomposition (EEMD) combined with adaptive thresholding. A signal was decomposed into individual components called intrinsic mode functions (IMFs). Each decomposed signal was then compared with those IMFs resulting from a white-noise realization to determine if the original signal contained structural features or white noise only. A thresholding scheme then removed all nonstructured portions. Our scheme is very flexible, and it is applicable in a variety of domains or in a diverse set of data. For instance, it can serve as an alternative for random noise removal by band-pass filtering in the time domain or spatial prediction filtering in the frequency-offset domain to enhance the lateral coherence of seismic sections. We have determined its potential for microseismic and reflection seismic denoising by comparing its performance on synthetic and field data using a variety of methods including band-pass filtering, basis pursuit denoising, frequency-offset deconvolution, and frequency-offset empirical mode decomposition.

Aerial Bundled Cables (ABCs) installed in coastal regions experience accelerated ageing due to rapid insulation degradation. Such damage causes unexpected and frequent cable failure. Sophisticated NDT methods are hence required for effective condition monitoring of ABCs. The corona discharge generated in vicinity of deteriorating ABCs is a consequence of damaged insulation. The discharge can be measured using super-heterodyned airborne ultrasonic listening. So measured corona discharge is intermittent and non-stationary, along the length of the ABC. Therefore, the usefulness of advanced adaptive techniques including Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complementary Ensemble EMD (CEEMD) as Intrinsic Mode Functions (IMFs) extraction procedures is investigated for damage assessment of energized ABCs. Respective IMFs are used to compute Hilbert Huang Transform (HHT) spectrums. HHT spectrums are computed processing periodically acquired ultrasonic signals from different locations within the coastal region. All three IMF extraction techniques reveal a progressive shift in the signal energy spectrum towards lower frequency and, hence, are able to classify the health state of operational ABCs. The EMD, EEMD and CEEMD techniques are rated to be sequentially effective in damage quantification, with CEEMD being the most eloquent.

Ensemble empirical mode decomposition (EEMD) has been widely used in fault diagnosis of planetary gearboxes. However, artificially selecting the amplitude of white Gaussian noise (WGN) which is the crucial parameter of EEMD probably produces subjective errors. To alleviate this drawback, a self-adaptive EEMD method based on particle swarm optimization (PSO-EEMD) is proposed. This method takes the uniform distribution characteristic of signal extreme points as the fitness function of PSO in order to self-adaptively determine the optimal amplitude of WGN, which can improve the self-adaptability and decomposition precision of EEMD. Furthermore, utilizing the advantages of PSO-EEMD in extracting weak characteristics from noisy signals, a new fault diagnosis method of planetary gearboxes based on PSO-EEMD and frequency demodulation analysis is put forward. Simulation analysis results show that the PSO-EEMD method can self-adaptively determine the optimal amplitude of GWN according to the original signal characteristics and obtain more accurate intrinsic mode functions (IMFs) than the traditional EEMD, which illustrate the effectiveness and superiority of the proposed method. The fault diagnosis case of a planetary gearbox shows that the proposed fault diagnosis method based on PSO-EEMD and frequency demodulation analysis can effectively extract the characteristic frequency from the sun gear with a crack fault.