Use Of Empirical Mode Decomposition Research Articles

Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition.

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase "a" of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs.

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis, this approach does not require the establishment of arbitrary band limits. The study uses a multivariate EMD algorithm (APIT-MEMD) to extract IMFs from the EEG signals of 34 healthy volunteers. The first six IMFs are analyzed using two different methods, based on FFT and HHT, and the results compared using the ANOVA test and the Bland–Altman method for agreement test. The outcomes show that the frequency values of the first six IMFs fall within the range of classic EEG bands (1.72–52.4 Hz). Although there was a lack of agreement in the mean weighted frequency values of the first three IMFs between the two methods (>3 Hz), both methods showed similar results for power spectral density (<5% normalized units, %, of power spectral density). The HHT method is found to have better frequency resolution than APIT-MEMD associated with FTT that produce less overlapping between IMF3 and 4 (p = 0.0046) and it is recommended for analyzing the spectral properties of IMFs. The study concludes that the HHT method could help to avoid the assumption of strict frequency band limits, and that the potential impact of EEG physiological phenomenon on mode-mixing interpretation, particularly for the alpha and theta ranges, must be considered in future research.

The Use of Empirical Mode Decomposition on Heart Rate Variability Signals to Assess Autonomic Neuropathy Progression in Type 2 Diabetes

In this study, we investigated the use of empirical mode decomposition (EMD)-based features extracted from electrocardiogram (ECG) RR interval signals to differentiate between different levels of cardiovascular autonomic neuropathy (CAN) in patients with type 2 diabetes mellitus (T2DM). This study involved 60 participants divided into three groups: no CAN, subclinical CAN, and established CAN. Six EMD features (area of analytic signal representation—ASRarea; area of the ellipse evaluated from the second-order difference plot—SODParea; central tendency measure of SODP—SODPCTM; power spectral density (PSD) peak amplitude—PSDpkamp; PSD band power—PSDbpow; and PSD mean frequency—PSDmfreq) were extracted from the RR interval signals and compared between groups. The results revealed significant differences between the noCAN and estCAN individuals for all EMD features and their components, except for the PSDmfreq. However, only some EMD components of each feature showed significant differences between individuals with noCAN or estCAN and those with subCAN. This study found a pattern of decreasing ASRarea and SODParea values, an increasing SODPCTM value, and a reduction in PSDbpow and PSDpkamp values as the CAN progressed. These findings suggest that the EMD outcome measures could contribute to characterizing changes associated with CAN manifestation in individuals with T2DM.

Design of optimal Elman Recurrent Neural Network based prediction approach for biofuel production

Renewable sources like biofuels have gained significant attention to meet the rising demands of energy supply. Biofuels find useful in several domains of energy generation such as electricity, power, or transportation. Due to the environmental benefits of biofuel, it has gained significant attention in the automotive fuel market. Since the handiness of biofuels become essential, effective models are required to handle and predict the biofuel production in realtime. Deep learning techniques have become a significant technique to model and optimize bioprocesses. In this view, this study designs a new optimal Elman Recurrent Neural Network (OERNN) based prediction model for biofuel prediction, called OERNN-BPP. The OERNN-BPP technique pre-processes the raw data by the use of empirical mode decomposition and fine to coarse reconstruction model. In addition, ERNN model is applied to predict the productivity of biofuel. In order to improve the predictive performance of the ERNN model, a hyperparameter optimization process takes place using political optimizer (PO). The PO is used to optimally select the hyper parameters of the ERNN such as learning rate, batch size, momentum, and weight decay. On the benchmark dataset, a sizable number of simulations are run, and the outcomes are examined from several angles. The simulation results demonstrated the suggested model's advantage over more current methods for estimating the output of biofuels.

Investigating Empirical Mode Decomposition in the Parameter Estimation of a Three-Section Winding Model

Parameter estimation represents an important aspect of modeling electromagnetic systems, and a wide range of parameter estimation strategies has been explored in literature. Most parameter estimation methodologies make use of either time-domain or frequency-domain responses as measured from the terminals of the device under test. Very limited research has, however, been conducted into exploring the use of modal decomposition strategies on the time-domain waveforms for parameter estimation applications. In this paper, the use of Empirical Mode Decomposition for estimating the parameters of a three-section lumped parameter transformer model is explored. A novel approach is proposed to define the optimization cost function in terms of the intrinsic modes of simulated time-domain waveforms. The results are compared with results obtained using classical time-domain and frequency-domain approaches. It is shown through an impulse response test that weighting the modes obtained from the Inferred Empirical Mode Decomposition approach presented in this work offers advantages in terms of accurately representing the target model transfer function dynamics and can assist in interpreting the various dynamic modes associated with the target model.

An efficient machine learning based ventricular late potential detection and classification technique for cardiac healthcare

SUMMARYCardiovascular diseases (CVD) are a major cause of death worldwide each year, with myocardial infarction (MI) accounting for the highest percentage of deaths. The rate of mortality can be reduced if electrical homogeneity of the cardiac tissue is detected early. The presence of the ventricular late potential (VLP) at the conclusion of the QRS complex and the start of the ST segment of the electrocardiogram (ECG) signal indicates a violation. Existing research use a wavelet‐based decomposition of the ECG signal for VLP detection, which is a multistage process. The use of empirical mode decomposition (EMD) to automatically distinguish normal ECG signal and signal with the presence of VLP is proposed in this work. Furthermore, utilizing machine learning classifiers, this research aims to enable automated diagnosis of cardiac arrhythmia. Intrinsic mode functions (IMF) are amplitude and frequency modulated waves that result from the decomposition of ECG using the EMD. Each IMF's peak amplitudes, spectral entropy, and mean of the ECG in the EMD are retrieved and used as input features for various machine learning techniques (ML). According to simulation findings, the bagging classifier achieves accuracy and specificity of 98.93% and 98.62%, respectively, while the AdaBoost classifier based on machine learning technique achieves sensitivity of 99.84%. The observed performance is considered to be the best when compared to existing approaches.

Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier

Parkinson’s disease (PD) is one of the neurodegenerative diseases. The neuronal loss caused by this disease leads to symptoms such as lack of initiative, depressive states, psychological disorders, and impairment of cognitive functions as well as voice dysfunctions. This paper aims to propose a system of automatic recognition of Parkinson’s disease by voice analysis. In this system, we are based on a database of 38 recordings, 20 people with Parkinson’s disease and 18 healthy people pronounce the vowel /a/.at first, we have decomposed the vocal signal of each patient by the Empirical Mode Decomposition (EMD), then, we extract from 1 to 12 coefficients of the Mel Frequency Cepstral Coefficients (MFCC), to obtain the voiceprint from each voice sample, we compressed the frames by computing their average value. At the end of the classification, we have used the validation scheme “holdout” as well as the K-nearest neighbor (KNN) classifier, the performance of this classification gives accuracy up to 86,67% when applied to 80% of the database as training data.

Use of empirical mode decomposition and K- nearest neighbour classifier for rolling element bearing fault diagnosis

Rolling bearing is widely used in rotary machine; the unexpected failure affects the normal operation of the machine leads to production and financial losses to the industry. Hence, Rolling Element Bearing (REB) fault diagnosis is crucial. In this paper, raw bearing vibration signal is divided into the various intrinsic mode functions (IMFs) using Empirical Mode Decomposition (EMD). Statistical features are extracted from the first three IMFs of each conditions of the bearing. These features were given as inputs to K-NN classifier to classify different condition of the bearing.

Use of Empirical Mode Decomposition in ERP Analysis to Classify Familial Risk and Diagnostic Outcomes for Autism Spectrum Disorder.

Event-related potentials (ERPs) activated by faces and gaze processing are found in individuals with autism spectrum disorder (ASD) in the early stages of their development and may serve as a putative biomarker to supplement behavioral diagnosis. We present a novel approach to the classification of visual ERPs collected from 6-month-old infants using intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD). Selected features were used as inputs to two machine learning methods (support vector machines and k-nearest neighbors (k-NN)) using nested cross validation. Different runs were executed for the modelling and classification of the participants in the control and high-risk (HR) groups and the classification of diagnosis outcome within the high-risk group: HR-ASD and HR-noASD. The highest accuracy in the classification of familial risk was 88.44%, achieved using a support vector machine (SVM). A maximum accuracy of 74.00% for classifying infants at risk who go on to develop ASD vs. those who do not was achieved through k-NN. IMF-based extracted features were highly effective in classifying infants by risk status, but less effective by diagnostic outcome. Advanced signal analysis of ERPs integrated with machine learning may be considered a first step toward the development of an early biomarker for ASD.

Technical Analysis with Empirical Mode Decomposition: A Case in the Hong Kong Stock Market

This article studies the performance improvement of technical analysis with the application of empirical mode decomposition. The study employs data from the Hong Kong stock market and addresses issues arising from gaps in intraday data. The results support the use of empirical mode decomposition in technical analysis to improve performance. <b>TOPICS:</b>Technical analysis, emerging markets, statistical methods, performance measurement <b>Key Findings</b> ▪ Empirical mode decomposition improves technical analysis profitability via denoising before candlestick construction. ▪ Empirical results show 68 basis points of profitability improvement a year on average in the Hong Kong stock market. ▪ Empirical results are robust regardless of the methods to handle the lunch gap in the data.

Evaluating phase synchronization methods in fMRI: A comparison study and new approaches

In recent years there has been growing interest in measuring time-varying functional connectivity between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.

New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms

Algorithms based on Empirical Mode Decomposition (EMD) and Iterative Filtering (IF) are largely implemented for representing a signal as superposition of simpler well-behaved components called Intrinsic Mode Functions (IMFs). Although they are more suitable than traditional methods for the analysis of nonlinear and nonstationary signals, they could be easily misused if their known limitations, together with the assumptions they rely on, are not carefully considered. In this work, we examine the main pitfalls and provide caveats for the proper use of the EMD- and IF-based algorithms. Specifically, we address the problems related to boundary errors, to the presence of spikes or jumps in the signal and to the decomposition of highly-stochastic signals. The consequences of an improper usage of these techniques are discussed and clarified also by analysing real data and performing numerical simulations. Finally, we provide the reader with the best practices to maximize the quality and meaningfulness of the decomposition produced by these techniques. In particular, a technique for the extension of signal to reduce the boundary effects is proposed; a careful handling of spikes and jumps in the signal is suggested; the concept of multi-scale statistical analysis is presented to treat highly stochastic signals.

Power swing detection and blocking of the third zone of distance relays by the combined use of empirical‐mode decomposition and Hilbert transform

This study aimed to develop a new method for detecting power swings and faults that may occur during these swings based on the combined use of empirical-mode decomposition and Hilbert transform. Power swings including line disconnection, fault occurrence, large sudden changes in the demand load, and reclosing, can cause a distance relay to malfunction and sever healthy lines from the network. Therefore, power swings and their simultaneous faults can pose a major challenge to protect the power systems. The proposed method in this study was tested on the standard IEEE 39-bus network for different power swing and fault states. It was also implemented on a distance relay manufactured by Vebko-Amirkabir Co. The evaluations show that the proposed method completely outperforms the conventional industrial method and other available methods in the literature. Since the proper functioning of this method depends on the choice of a threshold value, the current study also provides a simple and practical way to determine this threshold.

Use of Empirical Mode Decomposition in Improving Neural Network Forecasting of Paddy Price

Since rice is a staple food in Malaysia, its price fluctuations pose risks to the producers, suppliers and consumers. Hence, an accurate prediction of paddy price is essential to aid the planning and decision-making in related organizations. The artificial neural network (ANN) has been widely used as a promising method for time series forecasting. In this paper, the effectiveness of integrating empirical mode decomposition (EMD) into an ANN model to forecast paddy price is investigated. The hybrid method is applied on a series of monthly paddy prices fromFebruary 1999 up toMay 2018 as recorded in the Malaysian Ringgit (MYR) per metric tons. The performance of the simple ANN model and the EMD-ANN model was measured and compared based on their root mean squared Error (RMSE), mean absolute error (MAE) and mean percentage error (MPE). This study finds that the integration of EMD into the neural network model improves the forecasting capabilities. The use of EMD in the ANN model made the forecast errors reduced significantly, and the RMSE was reduced by 0.012, MAE by 0.0002 and MPE by 0.0448.

Steering measurement decomposition for vehicle lane keeping – A study of driver behaviour

Steering control for vehicle lane keeping has attracted significant attention from both automotive industries and researchers. To describe intermittent pulse-like qualities imparted by drivers that are seen in real-world steering measurements, a pulse control model (PCM) is presented for vehicle lane keeping. Inspired by the PCM, a steering angle measurement is decomposed into a combination of trend, integrated sine components (ISCs) and sine components (SCs), where trend corresponds to the path curvature, ISCs to the heading angles, and SCs to the lateral positions. Trends are extracted through the use of empirical mode decomposition (EMD) and principal component analysis (PCA), with singular spectral analysis (SSA) and Fourier curve-fitting (FCF) being employed to determine the ISCs and SCs in the main pulses. Through statistical pattern analysis on experimental measurements of drivers’ steering performance, it is revealed that (1) the pulse steering behaviour from real drivers shows the benefit of the proposed PCM for steering control during lane keeping, and (2) classification of pulse steering characteristics can be used for normal driver state identification and highlight abnormal driving behaviour, leading to the prospect of identifying driving characteristics typical of impaired concentration, substance misuse or tiredness, for instance.

NIRS-based classification of clench force and speed motor imagery with the use of empirical mode decomposition for BCI

Near-infrared spectroscopy (NIRS) is a non-invasive optical technique used for brain–computer interface (BCI). This study aims to investigate the brain hemodynamic responses of clench force and speed motor imagery and extract task-relevant features to obtain better classification performance. Given the non-stationary characteristics of real hemodynamic measurements, empirical mode decomposition (EMD) was applied to reduce the physiological noise overwhelmed in the task-relevant NIRS signals. Compared with continuous wavelet decomposition, EMD does not require a pre-determined basis function. EMD decomposes the original signals into a set of intrinsic mode functions (IMFs). In this study, joint mutual information was applied to select the optimal features, and support vector machine was used as a classifier. Offline and pseudo-online analyses showed that the most feasible classification accuracy can be obtained using IMFs as input features. Accordingly, an alternative feature is provided to develop the NIRS–BCI system.

Machine fault detection by signal denoising—with application to industrial gas turbines

The paper proposes a new methodology of machine fault detection for industrial gas turbine (IGT) systems. The integrated use of empirical mode decomposition (EMD), principal component analysis (PCA) and Savitzky–Golay (S–G) adaptive filtering are applied to extract noise from underlying measurements. Through analysis of the resulting noise, along with the use of a developed power index, it is shown that transient measurements associated with system load or demand changes, for instance, can be effectively discriminated from those that are characteristic of emerging faults – the former being a primary contributor to generating ‘false alerts’ using more traditional techniques that are only robust when used with steady-state measurements. Comparative studies show the benefits of the hybrid technique compared to the more usual use of EMD and PCA alone. Three operational conditions are identifiable from the resulting noise analysis viz. normal behaviour, transient operation, and characteristics of emerging machine faults. The efficacy of the proposed approach is demonstrated using two experimental case studies (bearing wear and burner faults) on sub-15MW IGTs.

BEARING FAULT DETECTION USING ACOUSTIC EMISSION SIGNALS ANALYZED BY EMPIRICAL MODE DECOMPOSITION

In condition monitoring of ball bearings, traditional techniques involving vibration, acceleration may not be able to detect a growing fault due to the low impact energy generated by the relative motion of the components. This study presents an experimental evaluation for incipient fault detection of lightly loaded ball bearings by using acoustic emission method. A table top bearing test rig is fabricated to simulate bearing faults such as inner race & outer race defect etc., with rotating shaft carrying low loads. The effectiveness of the acoustic analysis technique is demonstrated through signal processing techniques; use of Empirical Mode Decomposition (EMD) method to derive the characteristics features related to defective bearings from time domain and enveloped spectra in the frequency domain. The results reveal that the acoustic technique is more effective in detecting bearings’ failure than that of the vibration measurement.

Time–frequency analysis for parametric and non-parametric identification of nonlinear dynamical systems

This paper points out the differences between linear and nonlinear system identification tasks, shows that time–frequency analysis is most appropriate for nonlinearity identification, and presents advanced signal processing techniques that combine time–frequency decomposition and perturbation methods for parametric and non-parametric identification of nonlinear dynamical systems. Hilbert–Huang transform (HHT) is a recent data-driven adaptive time–frequency analysis technique that combines the use of empirical mode decomposition (EMD) and Hilbert transform (HT). Because EMD does not use predetermined basis functions and function orthogonality for component extraction, HHT provides more concise component decomposition and more accurate time–frequency analysis than the short-time Fourier transform and wavelet transform for extraction of system characteristics and nonlinearities. However, HHT's accuracy seriously suffers from the end effect caused by the discontinuity-induced Gibbs' phenomenon. Moreover, because HHT requires a long set of data obtained by high-frequency sampling, it is not appropriate for online frequency tracking. This paper presents a conjugate-pair decomposition (CPD) method that requires only a few recent data points sampled at a low-frequency for sliding-window point-by-point adaptive time–frequency analysis and can be used for online frequency tracking. To improve adaptive time–frequency analysis, a methodology is developed by combining EMD and CPD for noise filtering in the time domain, reducing the end effect, and dissolving other mathematical and numerical problems in time–frequency analysis. For parametric identification of a nonlinear system, the methodology processes one steady-state response and/or one free damped transient response and uses amplitude-dependent dynamic characteristics derived from perturbation analysis to determine the type and order of nonlinearity and system parameters. For non-parametric identification, the methodology uses the maximum displacement states to determine the displacement–stiffness curve and the maximum velocity states to determine the velocity–damping curve. Numerical simulations and experimental verifications of several nonlinear discrete and continuous systems show that the proposed methodology can provide accurate parametric and non-parametric identifications of different nonlinear dynamical systems.

Improved Hybrid Intelligent Method for Urban Road Traffic Flow Forecasting based on Chaos-PSO Optimization

Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the low recognition rate. If using them independently, the prediction accuracy would be lower than integrated use. Hence, a new hybrid intelligent prediction approach base on the combination of advanced signal processing technique and intelligent data mining method is proposed for the short time traffic flow prediction in this paper. The new method is marked as the use of empirical mode decomposition (EMD) and support vector machine (SVM) to deal with the nonlinear and stochastic characteristics of the traffic flow data. Another advantage of the proposed method is that the Chaos-particle swarm optimization (PSO) is adopted for the optimization of the combination. By doing so, the local optimization of the EMD-SVM prediction model can be avoided and the forecasting rate can be enhanced significantly. The practical traffic flow data were applied to the validation of the proposed prediction model. The analysis results show that the proposed method can extract the underlying rules of the testing data and decrease prediction error by 0.53% or better when compared with single SVM approach. Thus, the new hybrid intelligent traffic flow forecasting model can provide practical application.