Vital Sign Signal Extraction Method Based on Permutation Entropy and EEMD Algorithm for Ultra-Wideband Radar

Automatic monitoring of group‐housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise‐dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise‐dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

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).

Ballistocardiogram (BCG) could reflect mechanical activity of cardiovascular system instead of ECG. And it is often acquired by sensitive mattress or chair without any constraints and limitations, but it contains many noise because of the impact of body and acquired equipment, those questions make heart rate detection difficult from the original BCG. In the paper, we propose an adaptive method which is used to extract heartbeat intervals (RR), and the method acquire automatically input parameters of Ensemble Empirical Mode Decomposition (EEMD) algorithm, and then decompose BCG signal using EEMD algorithm, and select adaptively decomposition component of BCG signal, whose periodicity is in accordance with the cardiac cycle completely as the target signal. Furthermore we detect the peak points and calculate the heartbeat intervals series using the target signal. In the result, the proposed method is tested using the BCG datasets from 18 subjects, including 8 females and 10 males (age 20–72). Finally, the heart rate from BCG will be compared with ECG, and the results are satisfactory and have a high accuracy.

Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self‐similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed (i) determining the local trends of precipitations, (ii) investigating precipitation nonlinear dynamics, and (iii) assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Fluctuation Analysis (MFDFA) method. To overcome the detrending issue in the standard MFDFA method, the EEMD algorithm is embedded into the MFDFA. The study period is subdivided into three subperiods: 1951–1970, 1971–1990, and 1991–2010. Intrinsic Mode Functions (IMFs) are obtained according to the climatic region in which the stations are located. Results show that precipitation variation is significantly governed by the five first IMFs, in which oscillation periods vary from 1 to 25 days. The trend curves decrease at all the synoptic stations, and their slope values vary accordingly to the subperiods. Referring to the values of the multifractal spectrum parameters, α0, and the width of the spectrum w, consistent changes are observed regardless of the subperiods and the concerned stations. The spatial and temporal variability of precipitation indicates that the multifractal properties are good indicators for assessing changes in precipitation dynamics, and the changes in their features could be explained by the global change than by the local climate variation (climatic zones). Despite the observed differences in multifractal spectra properties from the three subperiods, it is not possible to verify the subdivision of 1951–2010 in three subperiods as it is done by previous studies in West Africa. Our findings can be used in the validation of global and regional climate models since a valid model should explain empirically detected scaling properties in observed data.

Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition

In this work, a new instantaneous fundamental frequency extraction method is presented, with the attention especially focused on its robustness for pathological voices processing. It is based on the Ensemble Empirical Mode Decomposition (EEMD) algorithm, which is a completely data-driven method for signal decomposition into a sum of AM - FM components, called Intrinsic Mode Functions (IMFs) or modes. Our results show that the speech fundamental frequency can be captured in a single IMF. We also propose an algorithm for selecting the mode where the fundamental frequency can be found, based on the logarithm of the power of the IMFs. The instantaneous frequency is then extracted by means of well-known techniques. The behaviour of the proposed method is compared with other two ones (Robust Algorithm for Pitch Tracking -RAPT- and auto-correlation based algorithms), both in normal and pathological sustained vowels.

The acoustic emission (AE) method is used in certain industries for the measurement of pneumatic conveying. Instead of the non-intrusive sensors, the comparison of two different intrusive probes in pneumatic conveying is presented in this work, and the AE signals generated by the flow for different particle flow rates and particle sizes were studied. Comparing the distribution of root mean square (RMS) values indicates that the AE signal acquired by a wire mesh probe was more reliable than that from a T-type probe. Limited intrinsic mode functions (IMFs) were extracted from the raw signals by the ensemble empirical mode decomposition (EEMD) algorithm. The characteristics of these signals were analyzed in both the time and frequency domains, and the energies of different IMFs were used to predict the particle mass flow rates, demonstrating a relative error under 10% achieved by the proposed monitoring system. Additionally, the mean squared error contribution fraction, instead of the energy fraction, can predict the particle size.

Modifying the Hilbert-Huang transform using the nonlinear entropy-based features for early fault detection of ball bearings

This paper presents a variational mode decomposition (VMD)-based heart rate extraction algorithm to extract heart rate of the target under intermittent interference of non-stationary clutters caused by another person. When nonstationary clutters exist around the interested target, the heart rate detection becomes difficult for FMCW radars. In regard to the situation, VMD is utilized to extract the heartbeat signal and suppress the effect of non-stationary clutters after applying filter preprocessing. This approach is compared to other methods based on ensemble empirical mode decomposition (EEMD) and traditional fast Fourier transform (FFT). As a result, the proposed algorithm can achieve low computational load and high accuracy of heart rate extraction with relative error of 1.7%. The heartbeat signal extracted by the proposed algorithm has signal-to-noise ratio (SNR) improvement of 2.4 dB compared with EEMD algorithm.

Aiming at the problem of a large number of switch machines, harsh working environment and high frequency of equipment failures caused by many factors, in order to accurately diagnose railway switch machine faults, a switch machine fault diagnosis method based on adaptive neural fuzzy network (ANFIS) optimized by dynamic weight particle swarm (DPSO) algorithm is proposed by analyzing the vibration signals generated during the operation of the switch machine. Firstly, the working vibration signal is decomposed into several intrinsic mode functions (IMFs) and selected by using the ensemble empirical mode decomposition (EEMD) algorithm; then, the feature values of IMFs are extracted by using the improved time domain multiscale scatter extraction (ITMDE) algorithm, and then input into the optimized ANFIS model to learn and realize fault diagnosis; finally, the method is compared and analyzed with various diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose switch machine faults, which has certain reference significance for intelligent diagnosis of switch machine faults and related research in the future.

The carbon emission trading market is crucial for reducing emissions, conserving energy, and enhancing the climate and environment. Studying carbon price forecasting can encourage China's involvement in international carbon financial instruments trading and promote the development of China's pilot carbon markets. This study employs ensemble empirical mode decomposition (EEDM) to convert the initial carbon price, which is a non-stationary signal, into several intrinsic mode functions and residual terms. Subsequently, hybrid machine learning methods are used to estimate the carbon price. For empirical analysis, the three main carbon markets with large trading volumes, Guangdong, Hubei, and Shenzhen, are selected from the eight pilot carbon markets. To achieve short-term carbon price prediction, a hybrid model combining a genetic algorithm (GA) and back propagation (BP) neural network is used. This model effectively addresses the issue of neural networks falling into local optimization. The results indicate that the hybrid algorithm is significantly superior to other algorithms for short-term prediction. Additionally, another hybrid model, combining the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms, is employed to reduce forecast error while minimizing search parameters, which is not possible with traditional neural network models.

SummaryIn order to improve the prediction accuracy of network traffic, a novel prediction model based on ensemble empirical mode decomposition and multiple models is proposed. In this study, ensemble empirical mode decomposition algorithm is introduced to decompose the network traffic and get several components. Approximate entropy is introduced to judge the complexity of each component. According to the results of approximate entropy, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is introduced to predict low complexity components. The advantages of each prediction model are used to predict the appropriate component. The final prediction results are obtained by adding the predicted values of each component. In order to solve the problem that the prediction performance of support vector machine and echo state network is affected by their parameters, an improved whale optimization algorithm is proposed to optimize the parameters of the model. Meanwhile, the calculation results of approximate entropy show that compared with the original network traffic, the complexity of each component obtained by ensemble empirical mode decomposition is reduced, which reduces the complexity of modeling. Three network traffic datasets with sampling periods of 10 ms, 1 s, and 10 min are collected. Compared with the other five state‐of‐the‐art prediction models, the case study results show that the proposed prediction model has better prediction accuracy and excellent statistical performance indicators.

Although the induction motor (IM) is known for its robustness and low cost of construction, it can happen that the it presents electrical, magnetic or mechanical faults. In this paper, a Hilbert-Huang Transform (HHT) spectral envelope of the stator current signature is proposed for the diagnosis of an IM rotor broken bar (RBB) fault. Firstly, the classical well-known spectral analysis Fast Fourier Transform (FFT) technique is applied to the stator current signal to obtain the harmonic frequency characterizing the RBB fault. Secondly, the HHT technique based on the ensemble empirical mode decomposition (EEMD) algorithm is proposed and applied to the stator current signal to obtain a function called the intrinsic mode function (IMF) containing the frequency that is related to the harmonic frequency characterizing the RBB fault. A comparative study is then carried out to illustrate the HHT technique merits and its superiority with respect to the FFT classical one. To test the effectiveness of the proposed HHT technique and validate the simulation results obtained, several experimental tests are also conducted using a test bench.

The power quality signals are always mixed with noise, which will influence the accurate measurement and characteristic extraction. Therefore, it’s important to denoise for the signal processing. This paper puts forward a novel denoising algorithm for power quality disturbances (PQDs) based on variational mode decomposition (VMD) and the truth that the energy density times the average period of each intrinsic mode function (IMF) is a constant. Firstly, the VMD algorithm is used to decompose the white noise into several IMF components. Then, the average period energy product of each IMF component is calculated, and some of the IMF components are selected for reconstruction according to the judgment criterion to obtain the denoised signal. The simulation results show that the proposed algorithm can greatly improve the signal-to-noise ratio (SNR), and the denoising effect is better than that of wavelet threshold and ensemble empirical mode decomposition (EEMD) algorithm.

Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising