Single Channel Blind Source Separation Based on Dual-Tree Complex Wavelet Transform And Ensemble Empirical Mode Decomposition

Massive multiple input multiple output (MIMO) is the key technology in the 5G communication network. Blind source separation (BSS) plays an important role in MIMO communication when information about the transmitted signals or channel is not available. Independent component analysis (ICA) is one of the well-known BSS technique applied in MIMO wireless communication. Existing BSS techniques like ICA assumes number of receiving antennas to be greater than or equal to the number of transmit antennas and hence cannot be applied to multiple input single output (MISO) communication scenario, that happens during downlink massive MIMO communication. In single channel blind source separation (SCBSS) the source signals are retrieved from a single observed mixture (single channel or single receiving antenna) of the source signals. In this paper, we investigate the application of SCBSS to MISO communication system. Using ensemble empirical mode decomposition (EEMD), the MISO communication system is converted to a pseudo MIMO system followed by the application of principal component analysis (PCA) and ICA algorithms to separate the transmitted signals. The algorithm is successfully applied to 2 X 1 MISO system and with a slight modification to n X 1 MISO system. Simulation results are provided to validate the performance of the algorithm.

Feature mode decomposition (FMD) has advantages over the other newer time-frequency methods such as ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) in single channel blind source separation (SCBSS). However, FMD has the defect of needing to determine the precise number of fault sources manually. To solve the above defect of FMD, an adaptive method for determining the number of fault sources based on the shift invariant sparse code (SISC) is proposed. SISC was used to train a set of basis functions from the single channel signal, and the corresponding potential components were reconstructed firstly. Subsequently, the structural similarity of these potential components was used for clustering, and each of the obtained clustering signals represented one kind of fault. Then the number of clustering was determined by minimizing the structural correlation among the clustering signals. It was considered that the source separation had achieved the best effect when the structural difference among the clusters was the largest, and the number of clustering at this time was used as the optimal estimated value, which was used as the modal inputs number of FMD calculation model to realize SCBSS of rolling bearing. Simulation and experimental analysis were carried out to verify the effectiveness of the proposed method, and its superiority was also verified through comparison.

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

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.

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.

Long-term monitoring of ECG via wearable monitoring systems has already been widely adopted to detect and prevent heart diseases. However, one of the main issues faced by wearable ECG monitoring systems is that motion artifacts significantly affect the systems' stability and reliability. Therefore, motion artifact reduction is a very challenging task in filtering and processing physiological signals. Based on the existing algorithms and ECG prior knowledge, in this paper, we propose an algorithm, CEEMDAN-IMFx-PCA-CICA, for motion artifact reduction in ambulatory ECG signals using single-channel blind source separation technique. Our algorithm first utilizes CEEMDAN to decompose the mixed signals into IMFs (intrinsic mode function) containing different source signal features, thereby forming new multi-dimensional signals. Using the correlation between IMFx (IMF component with the most ECG features) and each IMF, and PCA are then applied to reduce the dimension of each IMF. Finally, the blind separation of the source ECG signals is achieved by using CICA with IMFx as the constraint reference component. The results of our experiments indicate that our algorithm outperformed CEEMDAN-CICA, CEEMDAN-PCA-CICA, and improved CEEMDAN-PCA-CICA. Besides, the number of iterations of the CICA is significantly reduced; the separated source signal is better; the obtained result is stable. Furthermore, the separated ECG signal has a higher correlation with the source ECG signal and a lower RRMSE, especially in the case of high noise-to-signal ratios.

In this paper a modified ensemble empirical mode decomposition (EEMD) method is presented, which is named winning-EEMD (W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions (IMFs), are discussed in this method. The signal-to-noise ratio (SNR) is used to measure the amplitude of added noise and the winning number of IMFs (which results most frequency) is used to unify the number of IMFs. By this method, the calculation speed of decomposition is improved, and the relative error between original data and sum of decompositions is reduced. In addition, the feasibility and effectiveness of this method are proved by the example of the oceanic internal solitary wave.

Blind single-channel source separation is a long-standing machine learning and signal processing problem. Traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints. Generative adversarial network (GAN) are free from statistical constraints and samples, but the role of adversarial training in the task of BSS has not been fully demonstrated. Therefore, a new separation network model that enables to learn the known separated signal distribution from the stepwise fine estimation of the unknown mixture distribution was presented in this paper, and a self-attention mechanism was introduced to solve the problem of the blurring details of the generated image by the generator which preserves image details in the process of image separation. Compared with the existing single-channel blind source separation algorithm based on generative adversarial network-neural egg separation (NES), the detailed information of this new separation algorithm is more prominent, and the source signal in the mixed image has been separated more effectively, and has better separation performance than the classic blind source separation algorithms.

Blind source separation plays an important role in extracting the source components from one or more mixture(s) of the sources received by a sensor or receiver. It is blind since no other information besides the observed mixture signals is available. In presence of only one observed mixture, it is known as single channel blind source separation (SCBSS). This paper proposes a method of SCBSS based on variational mode decomposition (VMD) and principal component analysis (PCA). The observed signal is decomposed into a number of modes simultaneously using VMD. Then, PCA is used to select the corresponding source components from the decomposed modes. An example illustrating the separation of a sinusoid and speech signal by our proposed method has been presented. Cross-correlation coefficient has been used to measure the performance of the VMD-PCA algorithm. Our method has been compared with the single channel independent component analysis (SCICA) and ensemble empirical mode decomposition-principal component analysis-independent component analysis (EEMD-PCA-ICA) methods in terms of quality of performance and computational complexity.

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.

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).

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.

A single-channel mixed and complicated nonlinear electromagnetic interference (EMI) signal generally can only be measured in the electromagnetic compatibility test for an electrical system. Based on the single-channel signal, this paper describes a novel identification method of EMI sources. The method applies the ensemble empirical mode decomposition (EEMD) for channel expansion. The number of main EMI sources is evaluated by the principal component analysis, and the main expanded channels are sifted by the correlation analysis to solve the underdetermined problem of single-channel blind source separation. This paper also describes an effective method to identify the interference strength among the main EMI sources: the amplitude ratios of main EMI sources are extracted by combining the frequency characteristics with intrinsic mode functions (IMFs), in which the frequency characteristics are obtained by fast independent component analysis and the IMFs are achieved by EEMD. When this method is applied for conducted EMI identification in switching power converters of aviation secondary power source, experimental results show that it can evaluate the number of main EMI sources and identify the main EMI sources accurately.

In order to extract the vibration feature of remanufactured engines, the vibration signal is decomposed by ensemble empirical mode decomposition(EEMD), and this method is applied to study vibration mode of remanufactured engines. Based on the decomposition of vibration signal, correlation coefficient is introduced to study the correlation between IMF(Intrinsic Mode Function) components and original signal, and sensitive factors of IMF components are calculated using correlation coefficients. Hilbert transform is made with sensitive IMF components. The study results show that vibration feature of remanufactured engines is include in IMF components decomposed by EEMD, the vibration modes of remanufactured engines can be differentiated by Hilbert-Huang spectrum and marginal spectrum based on sensitive IMF components. According to vibration feature, the vibration state of remanufactured engines is divided into cylinder wall, cylinder cover and crankshaft vibration modes, which are important to improve the remanufacture level of engines.

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.