Predicting displacement of bridge based on CEEMDAN-KELM model using GNSS monitoring data

AbstractBridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data.

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Mitigation of Ionospheric Scintillation Effects on GNSS Signals with VMD-MFDFA

Remote Sensing ◽

10.3390/rs11232867 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2867

Author(s):

Ahmed ◽

Wu ◽

Marlia ◽

Ednofri ◽

Zhao

Keyword(s):

Satellite System ◽

Ensemble Empirical Mode Decomposition ◽

Ionospheric Scintillation ◽

Fluctuation Analysis ◽

Intrinsic Mode Functions ◽

Multifractal Detrended Fluctuation Analysis ◽

Mode Decomposition ◽

Radio Signals ◽

Satellite Signal ◽

Global Navigation Satellite

Severe scintillations degrade the satellite signal intensity below the fade margin of satellite receivers thereby resulting in failure of communication, positioning, and navigational services. The performance of satellite receivers is obviously restricted by ionospheric scintillation effects, which may lead to signal degradation primarily due to the refraction, reflection, and scattering of radio signals. Thus, there is a need to develop an ionospheric scintillation detection and mitigation technique for robust satellite signal receivers. Hence, variational mode decomposition (VMD) is proposed. VMD addresses the problem of ionospheric scintillation effects on global navigation satellite system (GNSS) signals by extracting the noise from the radio signals in combination with multifractal detrended fluctuation analysis (MFDFA). MFDFA helps as a criterion designed to detect and distinguish the intrinsic mode functions (IMFs) into noisy (scintillated) and noise-free (non-scintillated) IMF signal components using the MFDFA threshold. The results of the proposed method are promising, reliable, and have the potential to mitigate ionospheric scintillation effects on both the synthetic (simulated) and real GNSS data obtained from Manado station (latitude 1.34° S and longitude 124.82° E), Indonesia. From the results, the effectiveness of VMD-MFDFA over complementary ensemble empirical mode decomposition with MFDFA (CEEMD-MFDFA) is an indication of better performance.

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Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

Computational Intelligence and Neuroscience ◽

10.1155/2016/7657054 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Jian Xiong ◽

Shulin Tian ◽

Chenglin Yang

Keyword(s):

Fault Diagnosis ◽

Relative Entropy ◽

Analog Circuits ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Diagnosis Method ◽

Eemd Method ◽

Learning Machine ◽

Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

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

Ultrasonic Imaging ◽

10.1177/0161734619900147 ◽

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.

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

Sensors ◽

10.3390/s19214751 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4751 ◽

Cited By ~ 2

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.

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Foetal heart rate assessment by empirical mode decomposition and spectral analysis

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2019-0096 ◽

2019 ◽

Vol 5 (1) ◽

pp. 381-383 ◽

Cited By ~ 1

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.

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

Journal of Mechanics ◽

10.1017/jmech.2020.19 ◽

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.

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

Entropy ◽

10.3390/e22091039 ◽

2020 ◽

Vol 22 (9) ◽

pp. 1039 ◽

Cited By ~ 1

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.

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

Symmetry ◽

10.3390/sym11040513 ◽

2019 ◽

Vol 11 (4) ◽

pp. 513 ◽

Cited By ~ 2

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.

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

Structural Health Monitoring ◽

10.1177/1475921718754372 ◽

2018 ◽

Vol 18 (2) ◽

pp. 347-375 ◽

Cited By ~ 9

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.

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Fusion-based Satellite Clock Bias Prediction Considering Characteristics and Fitted Residue

Journal of Navigation ◽

10.1017/s0373463317001035 ◽

2018 ◽

Vol 71 (4) ◽

pp. 955-970 ◽

Cited By ~ 8

Author(s):

Jicang Lu ◽

Chao Zhang ◽

Yong Zheng ◽

Ruopu Wang

Keyword(s):

Prediction Models ◽

Prediction Method ◽

Satellite System ◽

Satellite Clock ◽

Term Prediction ◽

Mode Decomposition ◽

Satellite Clock Bias ◽

Global Navigation Satellite ◽

Long Term Prediction

As Satellite Clock Bias (SCB) prediction may be affected by various factors such as periodic items, sampling length, and stochastic items, a fusion-based prediction method is proposed by considering characteristics of SCB and fitted residue. On this basis, an instance algorithm is presented by fusing four typical prediction models. First, we use Empirical Mode Decomposition (EMD) to pre-process and decompose the SCB series into multiple components with various characteristics. Then, we analyse the fitting performance of each model for different components and prediction length, namely short-, mid- and long-term prediction, and select models with the best performance. Next, we analyse fitted residue of the reconstructed SCB, and select the model with the best fitting results. Finally, we fuse the multiple selected models for SCB prediction. The method is tested using Global Positioning System (GPS) precise clock products provided by the International Global Navigation Satellite System Service (IGS). Experimental results show that, compared with single prediction models and existing combination models, the proposed fusion-based prediction method improves accuracy and stability. In particular, the proposed method is more stable and has better performance for mid- and long-term prediction.

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