Number Of Intrinsic Mode Functions Research Articles

Ensemble-empirical-mode-decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles.

Time delay estimation based on variational mode decomposition

In order to improve the time delay estimation of colored noise signals, this article proposes generalized cross-correlation time delay estimation based on variational mode decomposition. First of all, we put forward the signal energy detection criterion to extract the effective signal from the signal, which can reduce the amount of calculation and improve the real-time performance. Second, the effective signal is decomposed into a number of intrinsic mode functions using variational mode decomposition. The correlation coefficients of each intrinsic mode function and the original signal are calculated. The article reconstructed signal with intrinsic mode functions which extract useful intrinsic mode functions by defaulting the correlation coefficient threshold. Finally, this article uses generalized cross-correlation to estimate time delay of the reconstructed signal. Theoretical analysis and simulation results show that the accurate time delay estimation can be obtained under the condition of color noise by the proposed method. The measurement accuracy of the proposed method is 15 times that of the generalized cross-correlation, and the running time of the proposed method is 4.0601 times faster than that of the generalized cross-correlation algorithm. The proposed method can reduce the computation and the running time of the system and also improve the measurement accuracy.

A Deep Learning Prediction Model Based on Extreme‐Point Symmetric Mode Decomposition and Cluster Analysis

Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme‐point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c‐means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD‐DBN prediction model, EEMD‐DBN prediction model, CEEMD‐DBN prediction model, ESMD‐DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

Ensemble-empirical-mode-decomposition based micro-Doppler signal separation and classification

The target echo signals obtained by Synthetic Aperture Radar (SAR) and Ground Moving Target Indicator (GMTI) platforms are mainly composed of two parts, the micro-Doppler signal and the target body part signal. The wheeled vehicle and the track vehicle are classified according to the different character of their micro-Doppler signal. In order to overcome the mode mixing problem in Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) is employed to decompose the original signal into a number of Intrinsic Mode Functions (IMF). The correlation analysis is then carried out to select IMFs which have a relatively high correlation with the micro-Doppler signal. Thereafter, four discriminative features are extracted and Support Vector Machine (SVM) classifier is applied for classification. The experimental results show that the features extracted after EEMD decomposition are effective, with up 90% success rate for classification using one feature. In addition, these four features are complementary in different target velocity and azimuth angles.

A novel error compensation method for an absolute optical encoder based on empirical mode decomposition

Absolute optical encoders have emerged as a preferable choice of accurate positioning measurement for high-end manufacturing. To further improve the measurement accuracy of the absolute optical encoder, a novel error compensation method is proposed based on the empirical mode decomposition (EMD) method. This method is operated as follows: First, the measurement error achieved by an absolute optical encoder is decomposed into a number of intrinsic mode functions (IMFs) and the residue based on EMD. Then, a novel Hilbert marginal spectrum (HMS) based scheme is proposed to extract the underlying trend of the measurement error. Finally, the underlying trend is eliminated from the measurement error to improve the measurement accuracy. Experimental results indicate that the proposed compensation method extracts the underlying trend of the measurement error very well and improves the measurement accuracy of the absolute optical encoder.

A fault diagnosis method combined with ensemble empirical mode decomposition, base-scale entropy and clustering by fast search algorithm for roller bearings

A method based on ensemble empirical mode decomposition (EEMD), base-scale entropy (BSE) and clustering by fast search (CFS) algorithm for roller bearings faults diagnosis is presented in this study. Firstly, the different vibration signals were decomposed into a number of intrinsic mode functions (IMFs) by using EEMD method, then the correlation coefficient method was used to verify the correlation degree between each IMF and the corresponding original signals. Secondly, the first two IMF components were selected according to the value of correlation coefficient, each IMF entropy values was calculated by BSE, permutation entropy (PE), fuzzy entropy (FE) and sample entropy (SE) methods. Thirdly, comparing the elapsed time of BSE/PE/FE/SE models, using the first two IMF-BSE/PE/FE/SE entropy values as the input of CFS clustering algorithm. The CFS clustering algorithm did not require pre-set the number of clustering centers, the cluster centers were characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. Finally, the experiment results show that the computational efficiency of BSE model is faster than that of PE/FE/SE models under the same fault recognition accuracy rate, then the effect of fault recognition for roller bearings is good by using CFS method.

Illumination Variation-Resistant Video-Based Heart Rate Measurement Using Joint Blind Source Separation and Ensemble Empirical Mode Decomposition.

Recent studies have demonstrated that heart rate (HR) could be estimated using video data [e.g., exploring human facial regions of interest (ROIs)] under well-controlled conditions. However, in practice, the pulse signals may be contaminated by motions and illumination variations. In this paper, tackling the illumination variation challenge, we propose an illumination-robust framework using joint blind source separation (JBSS) and ensemble empirical mode decomposition (EEMD) to effectively evaluate HR from webcam videos. The framework takes the hypotheses that both facial ROI and background ROI have similar illumination variations. The background ROI is then considered as a noise reference sensor to denoise the facial signals by using the JBSS technique to extract the underlying illumination variation sources. Further, the reconstructed illumination-resisted green channel of the facial ROI is detrended and decomposed into a number of intrinsic mode functions using EEMD to estimate the HR. Experimental results demonstrated that the proposed framework could estimate HR more accurately than the state-of-the-art methods. The Bland-Altman plots showed that it led to better agreement with HR ground truth with the mean bias 1.15 beats/min (bpm), with 95% limits from -15.43 to 17.73 bpm, and the correlation coefficient 0.53. This study provides a promising solution for realistic noncontact and robust HR measurement applications.

Railway rolling bearing fault diagnosis based on multi-scale intrinsic mode function permutation entropy and extreme learning machine classifier

The application of the multi-scale intrinsic mode function permutation entropy and extreme learning machine classifiers in railway rolling bearing fault diagnosis is here proposed in this article. The original signal is first denoised using wavelet de-noising as a pre-filter, which improves the subsequent decomposition into a number of intrinsic mode functions using ensemble empirical mode decompose. Second, the multi-scale intrinsic mode function permutation entropy is extracted as feature parameters. Finally, the extracted features are entered into extreme learning machine for an automated fault diagnosis procedure. Case studies have been carried out to evaluate the validity of the approach. The results demonstrate its effectiveness for diagnosis of faults in railway rolling bearings.

Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis

A gearbox vibration signal is non-stationary and non-linear and has multiple components and multi-fractal properties, which make it difficult to effectively extract gearbox fault features. This paper proposes a method for gearbox fault feature extraction based on empirical mode decomposition (EMD) and multi-fractal detrended cross-correlation analysis (MFDCCA). First, EMD, a time-frequency analysis method, was employed to decompose the gearbox vibration signal into a number of intrinsic mode functions (IMFs). Second, the multi-fractal features hidden in the non-linear vibration signal were extracted by applying the MFDCCA to the selected major IMFs, thus highlighting the multi-fractality information which can be used to characterize different fault modes and severities of the gearbox. Third, for each IMF, three multi-fractal feature parameters sensitive to gearbox faults were selected from the multi-fractal features, further constructing the multi-fractal fault feature vector. Then, the principal component analysis (PCA) was introduced to reduce the dimensions of the extracted multi-fractal fault feature vectors and to enhance the accuracy of diagnosis. Finally, a radial basis function neural network was utilized to classify gearbox faults. Several commonly occurring faults were used to validate the proposed method in this study. Experimental results provide evidence that the extracted multi-fractal fault features can effectively distinguish different fault modes, even under slight variation in working conditions. Simultaneously, the results of comparison show that the performance of the proposed EMD-MFDCCA-PCA method outperforms that of EMD-MFDFA (multi-fractal detrended fluctuation analysis) combined with the traditional PCA.

New procedure for gear fault detection and diagnosis using instantaneous angular speed

Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

Heartbeat classification using different classifiers with non-linear feature extraction

The electrocardiogram (ECG) is an important technique for heart disease diagnosis. This paper proposes a novel method for ECG beat classification. Several important issues exist in the ECG beat classification, which, if suitably addressed, may lead to development of more robust and efficient recognizers. Some of these issues include feature extraction, choice of classification approach and optimization. A new method for non-linear feature extraction of ECG signals based on empirical mode decomposition (EMD), approximate entropy (ApEn) and wavelet packet entropy is presented. The proposed method first uses EMD to decompose ECG signals into a finite number of intrinsic mode functions (IMFs), calculates the ApEn of IMFs as one feature and then obtains the wavelet packet entropy of wavelet packet coefficients as another feature. The two features are regarded as a feature vector. The support vector machine (SVM) and probabilistic neural network (PNN) are used for beat classification. The particle swarm optimization algorithm is used to optimize parameters of the PNN and SVM. The performance of the SVM classifier is slightly superior to that of the PNN classifier with 98.6% accuracy.

Fault Diagnosis for Supporting Rollers of the Rotary Kiln Using the Dynamic Model and Empirical Mode Decomposition

Rotary kiln is key equipment widely used in the cement, metallurgical and environmental protection industry. The operation state of the rotary kiln was largely determined by the working condition of supporting rollers. This paper models the vibration mechanism of the supporting rollers under the rotary kiln crank caused by the internal thermal process. Based on the numerical analysis, a methodology based on empirical mode decomposition (EMD) for the fault diagnosis of the supporting rollers was proposed. By using EMD, the complicated vibration signal of the kiln can be decomposed into a number of intrinsic mode functions (IMFs). An analysis was made over the energy moment of IMF components to indicate the energy variation of the kiln harmonic and the roller harmonic of the vibration signals. Simulation and in field experiment results proved that the proposed method provided a validity of the approach for the condition monitoring and fault diagnosis of the low-speed machinery like the rotary kiln.DOI: http://dx.doi.org/10.5755/j01.mech.22.3.13072

High and low frequency unfolded partial least squares regression based on empirical mode decomposition for quantitative analysis of fuel oil samples

Accurate prediction of the model is fundamental to the successful analysis of complex samples. To utilize abundant information embedded over frequency and time domains, a novel regression model is presented for quantitative analysis of hydrocarbon contents in the fuel oil samples. The proposed method named as high and low frequency unfolded PLSR (HLUPLSR), which integrates empirical mode decomposition (EMD) and unfolded strategy with partial least squares regression (PLSR). In the proposed method, the original signals are firstly decomposed into a finite number of intrinsic mode functions (IMFs) and a residue by EMD. Secondly, the former high frequency IMFs are summed as a high frequency matrix and the latter IMFs and residue are summed as a low frequency matrix. Finally, the two matrices are unfolded to an extended matrix in variable dimension, and then the PLSR model is built between the extended matrix and the target values. Coupled with Ultraviolet (UV) spectroscopy, HLUPLSR has been applied to determine hydrocarbon contents of light gas oil and diesel fuels samples. Comparing with single PLSR and other signal processing techniques, the proposed method shows superiority in prediction ability and better model interpretation. Therefore, HLUPLSR method provides a promising tool for quantitative analysis of complex samples.

Fault detection, isolation, and diagnosis of status self-validating gas sensor arrays.

The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays.

A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time–frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time–frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

Hybrid de-noising approach for fiber optic gyroscopes combining improved empirical mode decomposition and forward linear prediction algorithms.

A noise reduction algorithm based on an improved empirical mode decomposition (EMD) and forward linear prediction (FLP) is proposed for the fiber optic gyroscope (FOG). Referred to as the EMD-FLP algorithm, it was developed to decompose the FOG outputs into a number of intrinsic mode functions (IMFs) after which mode manipulations are performed to select noise-only IMFs, mixed IMFs, and residual IMFs. The FLP algorithm is then employed to process the mixed IMFs, from which the refined IMFs components are reconstructed to produce the final de-noising results. This hybrid approach is applied to, and verified using, both simulated signals and experimental FOG outputs. The results from the applications show that the method eliminates noise more effectively than the conventional EMD or FLP methods and decreases the standard deviations of the FOG outputs after de-noising from 0.17 to 0.026 under sweep frequency vibration and from 0.22 to 0.024 under fixed frequency vibration.

Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China

Reducing the dependence on fossil-fuel-based resources is becoming significant due to the detrimental effects on environment and global energy-dependent. Thus, increased attention has been paid to wind power, a type of clean and renewable energy. However, owing to the stochastic nature of wind speed, it is essential to build a wind speed forecasting model with high-precision for wind power utilization. Therefore, this paper proposes a hybrid model which combines fast ensemble empirical model decomposition (FEEMD) with regularized extreme learning machine (RELM). The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs) and one residual series. Then RELM is built to forecast the sub-series. Partial auto correlation function (PACF) is applied to analyze the intrinsic relationships between the historical speeds so as to select the inputs of RELM. To verify the developed models, short-term wind speed data in July 2010 and monthly data from January 2000 to May 2010 in Hong songwa wind farm, Chengde city are used for model construction and testing. Two additional forecasting cases in Hebei province are also applied to prove the model’s validity. The simulation test results show that the built model is effective, efficient and practicable.

Effect of intermittent hypoxic training on hypoxia tolerance based on single-channel EEG

A single-channel algorithm was proposed in order to study effect of intermittent hypoxic training on hypoxia tolerance based on EEG pattern. EEG was decomposed by ensemble empirical mode decomposition into a finite number of intrinsic mode functions (IMFs) based on the intrinsic local characteristic time scale. Analytic amplitude, analytic frequency, and recurrence property quantified by recurrence quantification analysis were explored on IMFs, and the first two scales revealed difference between normal EEG and hypoxia EEG. Classification accuracy of hypoxia EEG and normal EEG could reach 67.8% before decline of neurobehavioral ability, which represented that hypoxia EEG pattern could be detected at an early stage. Classification accuracy of hypoxia EEG and normal EEG increased with time and deepened intensity of hypoxia was observed by regular shift of hypoxia EEG pattern with time in a three dimensional subspace. The reduced shift and classification accuracy after intermittent hypoxic training represented that hypoxia tolerance enhanced.

Voiced/unvoiced speech classification‐based adaptive filtering of decomposed empirical modes for speech enhancement

This study presents a speech filtering method exploiting the combined effects of the empirical mode decomposition (EMD) and the local statistics of the speech signal using the adaptive centre weighted average (ACWA) filter. The novelty lies in incorporating the frame class (voiced/unvoiced) in the conventional filtering using the EMD and the ACWA filter. The speech signal is segmented into frames and each one is broken down by the EMD into a finite number of intrinsic mode functions (IMFs). The number of filtered IMFs depends on whether the frame is voiced or unvoiced. An energy criterion is used to identify voiced frames while a stationarity index distinguishes between unvoiced and transient sequences. Reported results obtained on signals corrupted by additive noise (white, F16, factory) show that the proposed filtering in line with the frame class is very effective in removing noise components from noisy speech signal. Compared with filtering results of the wavelet, the ACWA, and the EMD-ACWA methods, the proposed technique gives much better results in terms of average segmental signal-to-noise ratio and listening quality based on perceptual evaluation speech quality score.