scholarly journals A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4751 ◽  
Author(s):  
Xiaoling Li ◽  
Bin Liu ◽  
Yang Liu ◽  
Jiawei Li ◽  
Jiarui Lai ◽  
...  
Keyword(s):  
Signal Detection ◽  
Spectrum Analysis ◽  
Doppler Radar ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Signal Separation ◽  
Radar Signal ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.

Adaptive Ultrasound Tissue Harmonic Imaging Based on an Improved Ensemble Empirical Mode Decomposition Algorithm

2020 ◽  
Vol 42 (2) ◽  
pp. 57-73
Author(s):  
Suya Han ◽  
Yufeng Zhang ◽  
Keyan Wu ◽  
Bingbing He ◽  
Kexin Zhang ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Second Harmonic ◽  
Ensemble Empirical Mode Decomposition ◽  
Harmonic Imaging ◽  
Motion Artifacts ◽  
Intrinsic Mode Functions ◽  
Tissue Harmonic Imaging ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Harmonic Components

Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

A New Rotor Fault Diagnosis Method Based on EEMD Sample Entropy and Grey Relation Degree

2013 ◽  
Vol 347-350 ◽  
pp. 426-429 ◽  
Author(s):  
Wen Bin Zhang ◽  
Yan Jie Zhou ◽  
Jia Xing Zhu ◽  
Ya Song Pu
Keyword(s):  
Fault Diagnosis ◽  
Rank Order ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Sampled Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Grey Relation

In this paper, a new rotor fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by rank-order morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs). Thirdly, some IMFs containing the most dominant fault information were calculated the sample entropy for four rotor conditions. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in rotor fault diagnosis. Its suitable for on-line monitoring and diagnosis of rotating machinery.

A blind signal detection algorithm for passive location system based on troposcatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1128-1133
Author(s):  
Zan Liu ◽  
Xihong Chen ◽  
Qiang Liu ◽  
Zedong Xie
Keyword(s):  
Signal Detection ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Passive Location ◽  
Intrinsic Mode Functions ◽  
Location System ◽  
Blind Signal ◽  
Mode Decomposition ◽  
Signal Simulation

AbstractTo improve detection performance of passive location system based on troposcatter, we propose a blind signal detection algorithm. According to our algorithm, complementary ensemble empirical mode decomposition decomposes the received signal into several intrinsic mode functions (IMFs). To reconstruct the signal and background noises, difference between the entropy of adjacent IMFs is utilized as a standard. Different IMFs are utilized to estimate threshold of energy detection algorithm and energy level of received signal. Simulation examples indicate that the proposed algorithm can blindly and effectively detect the signal.

scholarly journals Foetal heart rate assessment by empirical mode decomposition and spectral analysis

2019 ◽  
Vol 5 (1) ◽  
pp. 381-383 ◽  
Author(s):  
Patricio Fuentealba ◽  
Alfredo Illanes ◽  
Frank Ortmeier
Keyword(s):  
Heart Rate ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Spectral Information ◽  
P Value ◽  
Foetal Heart Rate ◽  
Foetal Heart ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise

AbstractThis paper focuses on studying the time-variant dynamics involved in the foetal heart rate (FHR) response resulting from the autonomic nervous system modulation. It provides a comprehensive analysis of such dynamics by relating the spectral information involved in the FHR signal with foetal physiological characteristics. This approach is based on two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modelling. First, the CEEMDAN allows to decompose the signal into intrinsic mode functions (IMFs). Then, the TV-AR modelling allows to analyse their spectral dynamics. Results reveal that the IMFs can involve significant spectral information (p -value < 0.05) that can help to assess the foetal condition.

Applications of Ceemdan in Dynamic Behavior of Defected Spur Gearbox Running Under Acyclism Regime

2020 ◽  
Vol 36 (6) ◽  
pp. 825-839
Author(s):  
A. Hammami ◽  
A. Hmida ◽  
M. T. Khabou ◽  
F. Chaari ◽  
M. Haddar ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Empirical Mode Decomposition ◽  
Combustion Engine ◽  
Stationary Regime ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Emd Method ◽  
Spur Gearbox

ABSTRACTEmpirical Mode Decomposition (EMD) and its approaches are powerful techniques in signal processing especially for the diagnosis of rotating machinery running in non-stationary regime. We are interested in this paper to the dynamic behavior of a defected one stage gearbox equipped with an elastic coupling and loaded under acyclism regime generated by a combustion engine. Actually, we adopt an approach to the EMD method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) as a technique to perform the diagnosis of the studied system. Since the obtained signals are modulated, all obtained Intrinsic Mode Functions (IMFs) are modulated and are processed and shown by the Wigner-Ville distributions (WVD) as well as the spectrum of their envelope in order to detect defects such as cracked tooth defect in the wheel of the spur gearbox and eccentricity defect in the gear.

scholarly journals A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1039 ◽  
Author(s):  
Haikun Shang ◽  
Yucai Li ◽  
Junyan Xu ◽  
Bing Qi ◽  
Jinliang Yin
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal Reconstruction ◽  
Partial Discharge ◽  
Hybrid Approach ◽  
Approximate Entropy ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Fusion Algorithm ◽  
Mode Decomposition ◽  
Adaptive Noise

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

2013 ◽  
Vol 694-697 ◽  
pp. 1151-1154
Author(s):  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Jia Xing Zhu ◽  
Yan Ping Su
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

scholarly journals Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 513 ◽  
Author(s):  
Fang Ma ◽  
Liwei Zhan ◽  
Chengwei Li ◽  
Zhenghui Li ◽  
Tingjian Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Superior Performance ◽  
Normal Operation ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Spurious Mode ◽  
Adaptive Noise

Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.

Damage localization under ambient excitations and non-stationary vibration signals by a new hybrid algorithm for feature extraction and multivariate distance correlation methods

2018 ◽  
Vol 18 (2) ◽  
pp. 347-375 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Moving Average ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Autoregressive Moving Average Model ◽  
Mode Decomposition ◽  
Stationary Signals ◽  
Moving Average Model ◽  
Adaptive Noise

Ambient excitations applied to structures may lead to non-stationary vibration responses. In such circumstances, it may be difficult or improper to extract meaningful and significant damage features through methods that mainly rely on the stationarity of data. This article proposes a new hybrid algorithm for feature extraction as a combination of a new adaptive signal decomposition method called improved complete ensemble empirical mode decomposition with adaptive noise and autoregressive moving average model. The major contribution of this algorithm is to address the important issue of feature extraction under ambient vibration and non-stationary signals. The improved complete ensemble empirical mode decomposition with adaptive noise method is an improvement on the well-known ensemble empirical mode decomposition technique by removing redundant intrinsic mode functions. In addition, a novel automatic approach is presented to select the most relevant intrinsic mode functions to damage based on the intrinsic mode function energy level. Fitting an autoregressive moving average model to each selected intrinsic mode function, the model residuals are extracted as the damage-sensitive features. The main limitation is that such features are high-dimensional multivariate time series data, which may make a difficult and time-consuming decision-making process for damage localization. Multivariate distance correlation methods are introduced to cope with this drawback and locate structural damage using the multivariate residual sets of the normal and damaged conditions. The accuracy and robustness of the proposed methods are validated by a numerical shear-building model and an experimental benchmark structure. The effects of sampling frequency and time duration are evaluated as well. Results demonstrate the effectiveness and capability of the proposed methods to extract sufficient and reliable features, identify damage location, and quantify damage severity under ambient excitations and non-stationary signals.

scholarly journals Application of EEMD Sample Entropy and Grey Relation Degree in Gearbox Fault Identification

2014 ◽  
Vol 8 (1) ◽  
pp. 402-408
Author(s):  
Wenbin Zhang ◽  
Libin Yu ◽  
Yanping Su ◽  
Jie Min ◽  
Yasong Pu
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Small Sample ◽  
Fault Identification ◽  
Morphological Filter ◽  
Sampled Data ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Grey Relation

In this paper, a new gearbox fault identification method was proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by mathematical morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EEMD method. Thirdly, some IMFs containing the most dominant fault information were calculated by the sample entropy for four gearbox conditions. Finally, since the grey relation degree has good classified capacity for small sample pattern identification, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gearbox fault diagnosis. It’s suitable for on-line monitoring and fault diagnosis of gearbox.

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