Intrinsic Mode Functions Signal Research Articles

Temperature Compensation for MEMS Accelerometer Based on a Fusion Algorithm.

This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization-back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal.

Speech emotion recognition using a combination of variational mode decomposition and Hilbert transform

The objective of automated speech emotion recognition (SER) is to recognize each emotion of the speech signal uniquely and efficiently using machines like computers, mobile devices, etc. The popularity of SER has been widely acknowledged among researchers due to its extensive applicability in practical contexts. The use of SER has shown to be advantageous in several domains, including medical treatment, enhancement of security systems, surveillance operations, online marketing strategies, online educational platforms, online search engines, personal communication, customer relationship management, reinforcement of machine and human connection, and numerous other areas. Numerous authors have used various techniques, including the combination of multiple features (acoustic, non-acoustic, or both) and classifiers (machine learning, deep learning, or both), in order to enhance the efficacy of emotion categorization. In the present work, our objective is to enhance the performance of emotion classification (PECL) by using a combined approach of the variational mode decomposition (VMD) and Hilbert transform (HT) techniques. Using the VMD method, we decomposed the speech signal frame into many sub-signals, or intrinsic mode functions (IMFs). Then HT is applied to each VMD-based IMF signal to find the mode instantaneous amplitude (MIA) and mode instantaneous frequency (MIF) signal vectors. We extracted proposed features such as HT-based approximate entropy (HTAE), HT-based permutation entropy (HTPE), HT-based increment entropy (HTIE), and HT-based sample entropy (HTSE) using each MIA and MIF signal vector. The combination of the proposed HT-based features is called HT-based entropy (HTE) features. Then, we accessed the PECL using the HTE and MFCC features alone and in conjunction with a deep neural network (DNN) classifier. The experiment's results showed that the combinations of the proposed feature (MFCC + HTE) using a DNN classifier outperformed the individual features and obtained a SER accuracy of 86.92% for the EMOVO dataset and 91.63% for the EMO-DB dataset.

Improvement of emotion classification performance using multi-resolution variational mode decomposition method

Automated speech emotion recognition (SER) has been gaining popularity among researchers for three decades because of its vast number of applications in the real world. It is helpful to improve the relationship between humans and machines, online marketing and education, customer relations, medical treatment, safe driving, online search, etc. Researchers adopt various methods to improve emotion recognition performance from speech signals, like using different combinations of features (acoustic, non-acoustic, or both), classifiers (machine learning, deep learning, or both). Our study tried to improve emotion classification performance using features based on the multi-resolution variational mode decomposition (MRVMD) method. We first decompose each signal frame into several sub-signals known as modes or intrinsic mode functions (IMFs) using the MRVMD method. Then the proposed features, multi-resolution variational mode mel-frequency cepstral coefficient (MRVMMFCC), multi-resolution variational mode approximate entropy (MRVMAE), and multi-resolution variational mode permutation entropy (MRVMPE), were extracted using the MRVMD-decomposed IMF signals. Finally, different combinations of the proposed features are used to classify the emotion using a deep neural network (DNN) classifier. From the experimental results, we found that combination of the proposed feature (MRVMMFCC + MRVMAE + MRVMPE) performs better than the other combination in recognizing emotion using speech signals. The proposed feature combination with a DNN classifier achieved an emotion classification accuracy of 83.4%, 85.01%, and 90.51% for the SAVEE, EMOVO, and EMO-DB datasets, respectively. We found that the proposed MRVMD method performed better than the state-of-the-art methods.

Temperature Drift Compensation of Fiber Optic Gyroscopes Based on an Improved Method.

This study proposes an improved multi-scale permutation entropy complete ensemble empirical mode decomposition with adaptive noise (MPE-CEEMDAN) method based on adaptive Kalman filter (AKF) and grey wolf optimizer-least squares support vector machine (GWO-LSSVM). By establishing a temperature compensation model, the gyro temperature output signal is optimized and reconstructed, and a gyro output signal is obtained with better accuracy. Firstly, MPE-CEEMDAN is used to decompose the FOG output signal into several intrinsic mode functions (IMFs); then, the IMFs signal is divided into mixed noise, temperature drift, and other noise according to different frequencies. Secondly, the AKF method is used to denoise the mixed noise. Thirdly, in order to denoise the temperature drift, the fiber gyroscope temperature compensation model is established based on GWO-LSSVM, and the signal without temperature drift is obtained. Finally, the processed mixed noise, the processed temperature drift, the processed other noise, and the signal-dominated IMFs are reconstructed to acquire the improved output signal. The experimental results show that, by using the improved method, the output of a fiber optic gyroscope (FOG) ranging from -30 °C to 60 °C decreases, and the temperature drift dramatically declines. The factor of quantization noise (Q) reduces from 6.1269 × 10-3 to 1.0132 × 10-4, the factor of bias instability (B) reduces from 1.53 × 10-2 to 1 × 10-3, and the factor of random walk of angular velocity (N) reduces from 7.8034 × 10-4 to 7.2110 × 10-6. The improved algorithm can be adopted to denoise the output signal of the FOG with higher accuracy.

A Novel Method for Early Fatigue Damage Diagnosis in 316L Stainless Steel Formed by Selective Laser Melting Technology.

Early fatigue damage is an important factor affecting the service safety of 316L stainless steel parts formed by selective laser melting (SLM) technology. Nonlinear ultrasonic testing for early fatigue damage in SLM 316L stainless steel specimens was carried out. A new method for evaluation of early fatigue damage based on nonlinear ultrasonic testing was proposed. Empirical mode decomposition (EMD) was applied to the unsteady ultrasonic testing signal, and the signal was decomposed into multiple intrinsic mode functions (IMFs) that meet certain conditions; then, the specific IMF (ESI) containing the effective fatigue damage information was extracted. Lastly, fast Fourier transform (FFT) was applied to the specific IMF signal to obtain the required information to evaluate the damage in the measured part caused by fatigue. The results of nonlinear ultrasonic testing agreed well with transmission electron microscope experimental analysis and theoretical model of acoustic nonlinearity caused by dislocations. The change in nonlinear ultrasonic testing results reflected the generation and evolution of dislocation structure during the low-cycle fatigue regime of the SLM 316L stainless steel specimen and revealed the early fatigue damage mechanism of this metal part. Compared with the classical FFT method, the EMD-ESI-FFT method is more sensitive in identifying the early damage in SLM 316L stainless parts induced by fatigue loading, which is equivalent to improving the early fatigue damage identification and diagnosis ability and can better ensure the service safety of important metal parts.

Research on milling chatter identification of thin-walled parts based on incremental learning and multi-signal fusion

Chatter is a kind of self-excited vibration that often occurs in the milling process of thin-walled parts, which has become the main factor restricting production efficiency and quality. Due to the occurrence of chatter, the signal becomes more complex and unstable. In order to realize milling chatter detection of thin-walled parts, the method of multi-sensor signal fusion is used. A chatter detection method based on variational mode decomposition (VMD) and nonlinear dimensionless index is proposed by analyzing the characteristics of signals in time–frequency domain. Firstly, a series of intrinsic mode function (IMF) components are obtained by decomposing force and acceleration signals with VMD. When chatter occurs, the energy is transferred to the chatter frequency band. Each IMF signal’s nonlinear energy entropy (EE) is extracted to construct the feature vector. A support vector machine chatter identification model based on multi-sensor signal fusion is established. To solve the problem of model incremental updating, supervised learning and unsupervised learning are combined to provide a method for chatter detection.

Research on short‐term output power forecast model of wind farm based on neural network combination algorithm

AbstractReliable wind farm short‐term output power prediction plays a crucial role in the safety and economy of power system operation. By embedding multiple neural network methods, such as long short‐term memory (LSTM) neural network, multiple optimized BP neural network and wavelet neural network (WNN), combined with multiple methods based on signal decomposition and reconstruction to evaluate the short‐term prediction effect. In this study, the variational modal decomposition (VMD) method and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are used to reduce the fluctuation of the original wind power signal. Besides, the intrinsic mode function (IMF) signals obtained by VMD and CEEMDAN methods are used. By predicting the IMF signals, the prediction error of each signal can be reduced, and the accuracy of wind power prediction can be indirectly improved. In order to test the validity of the proposed composite model, the validation prediction and rolling prediction of 72 h with a resolution of 10 min were carried out, respectively, using the data of Caijiagou wind farm in 2018. Through experiments, the relationship between the two predictions is analyzed, which provides an important reference for the selection of the prediction model. Corresponding results show that the proposed VMD‐LSTM and CEEMDAN‐LSTM methods significantly outperform other benchmark methods and provide very satisfactory results in both the accuracy and stability of wind farm power prediction.

A Frequency-Domain Location Method for Defects in Cables Based on Power Spectral Density

This paper proposes a cable defect location method based on the power spectral density (PSD) to improve the accuracy of existing frequency domain reflection (FDR) method. The principle of the proposed method is that empirical mode decomposition (EMD) is used to decompose the envelope of the reflection coefficient into the intrinsic mode function (IMF) of each insulation defect point and the real part of each IMF signal is windowed. Then, according to the characteristics of a random signal with a definite auto-correlation function, PSD analysis is performed on the IMF signal with limited power. In theory, the power spectrum of a stationary random process is a deterministic function, which transforms the analysis of uncertain signals into the analysis of deterministic functions from the mathematical principle. And combined with the FDR method, localizes the cable insulation defects. Finally, the feasibility and effectiveness of the proposed method are verified by simulation and experiment.

A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition

The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it’s hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Moreover, sometimes, deep-learning-based techniques, using raw images, fail to detect the infection. Hence, this paper represents a hybrid method employing both traditional signal processing and deep learning technique for quick detection of SARS-CoV-2 patients based on the CT-scan and Chest-X-ray images of a patient. Unlike the other AI-based methods, here, a CT-scan/Chest-X-ray image is decomposed by two-dimensional Empirical Mode Decomposition (2DEMD), and it generates different orders of Intrinsic Mode Functions (IMFs). Next, The decomposed IMF signals are fed into a deep Convolutional Neural Network (CNN) for feature extraction and classification of Covid patients and Non-Covid patients. The proposed method is validated on three publicly available SARS-CoV-2 data sets using two deep CNN architectures. In all the databases, the modified CT-scan/Chest-X-ray image provides a better result than the raw image in terms of classification accuracy of two fundamental CNNs. This paper represents a new viewpoint of extracting preprocessed features from the raw image using 2DEMD.

Detection of Target Frequency from SSVEP Signal Using Empirical Mode Decomposition for SSVEP Based BCI Inference System

This paper describes the effectiveness of feature obtained by power spectrum analysis (PSA) as well as the combined method of empirical mode decomposition (EMD) and PSA for the development of brain–computer interface (BCI) system using steady-state visual evoked potential (SSVEP). Accurate detection of SSVEP response from the recorded EEG signal is a difficult task for a new development of the BCI inference system. The EMD technique is a non-linear method of signal decomposition, which generates several intrinsic mode functions (IMFs) of different flickering frequencies. Prominent IMF signal of SSVEP plays a vital role in the accurate detection of frequency. The proposed method achieves the average detection accuracy of 81.45% over four subjects; in contrast, the conventional method of PSA achieves average detection accuracy of 80.43%. The achieved result indicates that the proposed method out performs state of the art by more than 1.02% over four subjects.

A Traveling Wave-Based Fault Location Method Employing VMD-TEO for Distribution Network

In this paper, a traveling wave based fault location method is proposed for distribution networks. Before fault occurs, an intrinsic distance difference matrix (IDDM) is established based on the characteristics of traveling wave propagation and the topology of distribution network. After the fault, the initial fault traveling wave signals are decomposed into several intrinsic mode function (IMF) signals by variational mode decomposition (VMD), and the first IMF signal is analyzed by teager energy operator (TEO) to determine the arriving time of initial fault traveling waves. According to the double-ended traveling wave method and the arrival time of traveling wave at all recorders, a fault distance difference matrix (FDDM) is built. By comparing the difference between IDDM and FDDM, a fault branch determination matrix (FBDM) and the lateral index Q are used to locate the fault. The effectiveness of the proposed method is verified via EMTP/ATP simulations. Results show that the proposed method has a high fault location accuracy, and does not need that all terminals of lines are installed traveling wave recorders.

Empirical Mode Decomposition, Viterbi and Wavelets Applied to Electrocardiogram Noise Removal

The electrocardiogram (ECG) signal is generally used as a cardiovascular disease diagnostic tool. The accuracy of the diagnosis is directly related to the quality of the ECG signal, which can be corrupted by several sources of noises such as, for example, baseline wanders and power line interference. This paper proposes a new ECG denoising methodology based on wavelets, empirical mode decomposition (EMD), and Viterbi algorithm. The EMD decomposes the signal in intrinsic mode functions (IMFs), then each one of these IMFs is processed by the discrete wavelet transform through a decision process based on the Viterbi algorithm. We apply the proposed method to a synthetic ECG signal and three real ECG signals. The simulations results show that this novel methodology outperforms denoising schemes based on wavelets, empirical mode decomposition, and total variation.

Detection and classification of multi-complex power quality events in a smart grid using Hilbert–Huang transform and support vector machine

This article examines the potential ability of a chosen Hilbert–Huang transform (HHT) in detecting and identifying a multi-complex electric power quality events (PQE) signal in smart grid power systems under various noise situations. HHT is an active signal processing technique, comprising the units of empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA) to detect the non-stationary electric signals. The function of EMD is to detect and decompose the non-stationary electric signal in different scales of frequency modules ranging from maximum to minimum values, and thereby the attained signals are characterized as intrinsic mode functions (IMFs). HSA details the IMF signals individually to produce a unique Hilbert spectrum which carries the information of original signal. By observing the instant time-varying deviations of frequency and amplitude of the resultant signals, it is possible to categorize the disturbing signals from the original signal. The discussed slots of work is simulated under MATLAB environment, and the results report that the HHT successfully detects the single PQE, complex PQE and multi-complex PQE signals under 25 dB, 50 dB and without noise situations. The outcomes of HHT technique are compared with other transforms such as S-transform and wavelet transform to highlight superior qualities of HHT. The identified PQE signals from HHT are classified using support vector machine to improve its classification accuracy. It is wiser to disclose that the proposed system with inbuilt monitoring and identification of PQE signals will suit present smart grid system.

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

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.

Detection of Subtle Damage in Structures through Smart Signal Reconstruction

The primary function of structural health monitoring (SHM) is the process of extracting the damage features from the measured raw data, recorded using sensors on the structure of interest. The efficiency of SHM techniques lies in their capability to detect early damage, which alters the dynamic characteristics of only a few modal responses but in a feeble manner, in its incipient stage. Isolating these modal responses, hidden in the overall raw response, for damage diagnosis, is a real challenge to the SHM community. In order to handle this issue, an improved version of Empirical Mode Decomposition (EMD) is employed in this paper. EMD decomposes the measured response signals into mono-component signals, called intrinsic mode functions (IMFs). The mixed modes in EMD are handled using Intermittency criteria in the proposed EMD. Once the IMFs are extracted from the raw signal, the IMFs (signal components) which possess the valuable information of incipient damage called ‘critical IMFs’, are isolated. To determine the spatial location of damage, these critical IMFs are combined to reconstruct a new signal with enriched information on minor/incipient damage. ARMAX model is employed on the new signal with enriched damage information. A normalized distance measure of ARMAX models, in terms of subspace angles, is used as a damage indicator. The numerical and experimental investigations presented in this paper clearly reflect that the proposed output-only damage diagnostic technique using the smart reconstruction of the measured raw signal is capable of detecting the incipient subtle damages in the structures.

Instantaneous Amplitude‐Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.

Degradation assessment of ball bearings utilizing curvilinear component analysis

The performance degradation assessment of ball bearings is of great importance to increase the efficiency and the reliability of rotating mechanical systems. The large dimensionality of feature space introduces a lot of noise and buries the potential information about faults hidden in the feature data. This paper proposes a novel health assessment method facilitated with two compatible methods, namely curvilinear component analysis and self-organizing map network. The novelty lies in the implementation of a vector quantization approach for the sub-manifolds in the feature space and to extract the fault signatures through nonlinear mapping technique. Curvilinear component analysis is a nonlinear mapping tool that can effectively represent the average manifold of the highly folded information and further preserves the local topology of the data. To answer the complications and to accomplish reliability and accuracy in bearing performance degradation assessment, the work is carried out with following steps; first, ensemble empirical mode decomposition is used to decompose the vibration signals into useful intrinsic mode functions; second, two fault features i.e. singular values and energy entropies are extracted from the envelopes of the intrinsic mode function signals; third, the extracted feature vectors under healthy conditions, further reduced with curvilinear component analysis are used to train the self-organizing map model; finally, the reduced test feature vectors are supplied to the trained self-organizing map and the confidence value is obtained. The effectiveness of the proposed technique is validated on three run-to-failure test signals with the different type of defects. The results indicate that the proposed technique detects the weak degradation earlier than the widely used indicators such as root mean square, kurtosis, self-organizing map-based minimum quantization error, and minimum quantization error-based on the principal component analysis.

Empirical mode decomposition processing to improve multifocal-visual-evoked-potential signal analysis in multiple sclerosis.

ObjectiveTo study the performance of multifocal-visual-evoked-potential (mfVEP) signals filtered using empirical mode decomposition (EMD) in discriminating, based on amplitude, between control and multiple sclerosis (MS) patient groups, and to reduce variability in interocular latency in control subjects.MethodsMfVEP signals were obtained from controls, clinically definitive MS and MS-risk progression patients (radiologically isolated syndrome (RIS) and clinically isolated syndrome (CIS)). The conventional method of processing mfVEPs consists of using a 1–35 Hz bandpass frequency filter (XDFT).The EMD algorithm was used to decompose the XDFT signals into several intrinsic mode functions (IMFs). This signal processing was assessed by computing the amplitudes and latencies of the XDFT and IMF signals (XEMD). The amplitudes from the full visual field and from ring 5 (9.8–15° eccentricity) were studied. The discrimination index was calculated between controls and patients. Interocular latency values were computed from the XDFT and XEMD signals in a control database to study variability.ResultsUsing the amplitude of the mfVEP signals filtered with EMD (XEMD) obtains higher discrimination index values than the conventional method when control, MS-risk progression (RIS and CIS) and MS subjects are studied. The lowest variability in interocular latency computations from the control patient database was obtained by comparing the XEMD signals with the XDFT signals. Even better results (amplitude discrimination and latency variability) were obtained in ring 5 (9.8–15° eccentricity of the visual field).ConclusionsFiltering mfVEP signals using the EMD algorithm will result in better identification of subjects at risk of developing MS and better accuracy in latency studies. This could be applied to assess visual cortex activity in MS diagnosis and evolution studies.

Empirical Mode Decomposition — Window Fractal (EMDWF) Algorithm in Classification of Fingerprint of Medicinal Herbs

This paper presents a new approach called the empirical mode decomposition — window fractal (EMDWF) algorithm in classification of fingerprint of medicinal herbs. In this way, we consider a glycyrrhiza fingerprint of medicinal herb as a signal sequence, and apply empirical mode decomposition (EMD) and Hiaguchis fractal dimension to construct a feature vector. By using EMD, the glycyrrhiza fingerprint of medicinal herb can be decomposed into some intrinsic mode functions (IMFs). As window fractal dimension (WFD) is applied to each IMF and original signal, the features of the glycyrrhiza fingerprint of medicinal herb can be obtained. Thereafter, SVM is applied as a classifier. The results of the experiments state clearly that the feature extracted by EMDWF is better than that of the existing methods including the pure EMD. With the increase of the number of training samples and the increase of the number of layers in EMD, the classification result achieves more stability.

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition.

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.