Empirical Mode Decomposition Approach Research Articles

EEMD-based notch filter for induction machine bearing faults detection

This paper deals with induction machine bearing faults detection based on an empirical mode decomposition approach combined to a statistical tool. In particular, it is proposed an innovative fault detector that is based on the dominant intrinsic mode function extraction, through an ensemble empirical mode decomposition, then its cancellation. The validation of this approach is based on simulations and experiments. The achieved simulation and experimental results clearly show that the proposed approach is well suited for bearing faults detection regardless the rank of the intrinsic mode function introduced by the fault.

Assessment of stable cutting zone in CNC turning based on empirical mode decomposition and genetic algorithm approach

Assessment of optimum stable cutting zone is the key requirement to maintain high productivity with enhanced surface quality of work-piece. Tool chatter is one of the factors responsible for deviation from these features. Despite the immense work done within this domain, still many aspects related to regenerative chatter remains unexplored. Usually, the chatter signals recorded from sensors are contaminated by background noise. Analysis of these contaminated signals results in faulty information regarding the identification of tool chatter. So, it becomes imperative that these signals should be denoised before further processing. In the present work, empirical mode decomposition technique has been adopted to pre-process the acquired raw chatter signals, which have been overlooked by the previous researchers. Initially, acoustic signals have been recorded by performing experiments at different combinations of cutting parameters. The preprocessed signals have been used to evaluate a new output parameter i.e. chatter index. Material removal rate has also been measured for each experiment. For estimating the dependence of output on input cutting parameters, mathematical models have been developed using response surface methodology. Moreover, the optimum cutting zone has been assessed by adopting multi-objective genetic algorithm. Finally, more experiments have been conducted to validate the obtained cutting zone. It has been found that the acquired cutting zone is capable of producing work pieces with good surface finish and acceptable material removal rate.

Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model

Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel SVR-based load forecasting model, namely EMD-PSO-GA-SVR, by hybridizing the empirical mode decomposition (EMD) with two evolutionary algorithms, i.e., particle swarm optimization (PSO) and the genetic algorithm (GA). The EMD approach is applied to decompose the load data pattern into sequent elements, with higher and lower frequencies. The PSO, with global optimizing ability, is employed to determine the three parameters of a SVR model with higher frequencies. On the contrary, for lower frequencies, the GA, which is based on evolutionary rules of selection and crossover, is used to select suitable values of the three parameters. Finally, the load data collected from the New York Independent System Operator (NYISO) in the United States of America (USA) and the New South Wales (NSW) in the Australian electricity market are used to construct the proposed model and to compare the performances among different competitive forecasting models. The experimental results demonstrate the superiority of the proposed model that it can provide more accurate forecasting results and the interpretability than others.

Composite quantile regression extreme learning machine with feature selection for short-term wind speed forecasting: A new approach

With the growing wind penetration of wind resources into power generation worldwide, accurate and comprehensive wind speed forecasting (WSF) is becoming increasingly significant to ensure continuously economical and reliable power system operations. In this paper, a novel WSF framework based on composite quantile regression outlier-robust extreme learning machine (CQR-ORELM) with feature selection and parameter optimization using a hybrid population-based algorithm is developed. The CQR-ORELM model offers flexibility and efficiency to explore potential nonlinear characteristic among wind speed variables and improve model robustness and predictive capability. A hybrid algorithm with the combination of particle swarm optimization and gravitational search algorithm (PSOGSA) is utilized to fine-tune the optimal value of weights and bias in the ORELM network structure, while the binary version of PSOGSA (BPSOGSA) is exploited asa feature selection method to construct the most relevant input feature matrix for the model. Aimed to alleviate the influence of uncertainty in wind speed time series, a time adaptive filter based empirical mode decomposition (TVF-EMD) approach is used to decompose the original wind speed series into several intrinsic mode functions (IMFs). Each IMF is well-developed using the proposed method and the final forecasting results are obtained through aggregate calculation. This proposed wind forecasting methodology is compared to several benchmarks. The effectiveness of the proposed forecast strategy to predict wind speed is evaluated by the trials of 10min ahead wind speed forecasting at two locations of the National Renewable Energy Laboratory (NREL). Comparative results confirm that the developed CQR-ORELM with feature selection procedure can describe the complete conditional distribution information hidden in variables and provide satisfactory wind speed information.

Low-frequency noise attenuation in seismic and microseismic data using mathematical morphological filtering

Low-frequency noise is one of the most common types of noise in seismic and microseismic data. Conventional approaches, such as the high-pass filtering method, utilize the low-frequency nature and distinguish between signal and noise based on their different frequency contents. However, conventional approaches are limited or even invalid when the signal and noise shares the same frequency band. Moreover, high-pass filtering method will suppress not only low-frequency noise but also low-frequency signal when they overlap in a same frequency band, which is extremely important in the inversion process for building the subsurface velocity model. To overcome the drawbacks of conventional high-pass filtering approach, we developed a novel method based on the mathematical morphology theorem to separate signal and noise using their differences in morphological scale. We extracted empirical relation between morphological scale and frequency band so that the mathematical morphology based approach can be conveniently used in low-frequency noise attenuation. The proposed method is termed as the mathematical morphological filtering (MMF) method. We compare the MMF approach with high-pass filtering and empirical mode decomposition (EMD) approaches using synthetic, reflection seismic and microseismic examples. The various examples demonstrate that the proposed MMF method can preserve more low-frequency signal than the high-pass filtering approach, and is more efficient and causes fewer artefacts than the EMD approach.

Diagnosis of non‐linear mixed multiple faults based on underdetermined blind source separation for wind turbine gearbox: simulation, testbed and realistic scenarios

The diagnosis of multi-fault in wind turbine gearbox based on vibration signal processing is considered challenging as the collected measurements from acceleration transducers are often a non-linear mixture of signals induced from an unknown number of sources, i.e. an underdetermined blind source separation (UBSS) problem. In this study, a novel UBSS-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery. The former is realised based on the empirical mode decomposition and singular value decomposition (SVD) joint approach; and for the latter, the observed vibration signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l1 norm decomposition are carried out to estimate the mixing matrix and recover the source signals, respectively. The performance of proposed solution is extensively assessed through experiments based on simulation, testbed and realistic wind farm measurements for a range of fault scenarios for both linear and non-linear scenarios. The numerical result clearly confirms the effectiveness of the proposed algorithmic solution for non-linear multi-fault diagnosis of wind turbines.

On the predictability of high water level along the US East Coast: can the Florida Current measurement be an indicator for flooding caused by remote forcing?

Recent studies show that in addition to wind and air pressure effects, a significant portion of the variability of coastal sea level (CSL) along the US East Coast can be attributed to non-local factors such as variations in the Gulf Stream and the North Atlantic circulation; these variations can cause unpredictable coastal flooding. The Florida Current transport (FCT) measurement across the Florida Straits monitors those variations, and thus, the study evaluated the potential of using the FCT as an indicator for anomalously high water level along the coast. Hourly water level data from 12 tide gauge stations over 12 years are used to construct records of maximum daily water levels (MDWL) that are compared with the daily FCT data. An empirical mode decomposition (EMD) approach is used to divide the data into high-frequency modes (periods T < ∼30 days), middle-frequency modes (∼30 days < T < ∼90 days), and low-frequency modes (∼90 days < T < ∼1 year). Two predictive measures are tested: FCT and FCT change (FCC). FCT is anti-correlated with MDWL in high-frequency modes but positively correlated with MDWL in low-frequency modes. FCC on the other hand is always anti-correlated with MDWL for all frequency bands, and the high water signal lags behind FCC for almost all stations, thus providing a potential predictive skill (i.e., whenever a weakening trend is detected in the FCT, anomalously high water is expected along the coast over the next few days). The MDWL-FCT correlation in the high-frequency modes is maximum in the lower Mid-Atlantic Bight, suggesting influence from the meandering Gulf Stream after it separates from the coast. However, the correlation in low-frequency modes is maximum in the South Atlantic Bight, suggesting impact from variations in the wind pattern over subtropical regions. The middle-frequency and low-frequency modes of the FCT seem to provide the best predictor for medium to large flooding events; it is estimated that ∼10–25% of the sea level variability in those modes can be attributed to variations in the FCT. An example from Hurricane Joaquin (September–October, 2015) demonstrates how an offshore storm that never made landfall can cause a weakening of the FCT and unexpected high water level and flooding along the US East Coast. A regression-prediction model based on the MDWL-FCT correlation shows some skill in estimating high water levels during past storms; the water level prediction is more accurate for slow-moving and offshore storms than it is for fast-moving storms. The study can help to improve water level prediction since current storm surge models rely on local wind but may ignore remote forcing.

A time varying filter approach for empirical mode decomposition

A modified version of empirical mode decomposition (EMD) is presented to solve the mode mixing problem. The sifting process is completed using a time varying filter technique. In this paper, the local cut-off frequency is adaptively designed by fully facilitating the instantaneous amplitude and frequency information. Then nonuniform B-spline approximation is adopted as a time varying filter. In order to solve the intermittence problem, a cut-off frequency realignment algorithm is also introduced. Aimed at improving the performance under low sampling rates, a bandwidth criterion for intrinsic mode function (IMF) is proposed. The proposed method is fully adaptive and suitable for the analysis of linear and non-stationary signals. Compared with EMD, the proposed method is able to improve the frequency separation performance, as well as the stability under low sampling rates. Besides, the proposed method is robust against noise interference.

Connection between solar activity cycles and grand minima generation

Aims. The revised dataset of sunspot and group numbers (released by WDC-SILSO) and the sunspot number reconstruction based on dendrochronologically dated radiocarbon concentrations have been analyzed to provide a deeper characterization of the solar activity main periodicities and to investigate the role of the Gleissberg and Suess cycles in the grand minima occurrence.Methods. Empirical mode decomposition (EMD) has been used to isolate the time behavior of the different solar activity periodicities. A general consistency among the results from all the analyzed datasets verifies the reliability of the EMD approach. Results. The analysis on the revised sunspot data indicates that the highest energy content is associated with the Schwabe cycle. In correspondence with the grand minima (Maunder and Dalton), the frequency of this cycle changes to longer timescales of ~14 yr. The Gleissberg and Suess cycles, with timescales of 60−120 yr and ~ 200−300 yr, respectively, represent the most energetic contribution to sunspot number reconstruction records and are both found to be characterized by multiple scales of oscillation. The grand minima generation and the origin of the two expected distinct types of grand minima, Maunder and longer Sporer-like, are naturally explained through the EMD approach. We found that the grand minima sequence is produced by the coupling between Gleissberg and Suess cycles, the latter being responsible for the most intense and longest Sporer-like minima (with typical duration longer than 80 yr). Finally, we identified a non-solar component, characterized by a very long scale oscillation of ~ 7000 yr, and the Hallstatt cycle (~ 2000 yr), likely due to the solar activity.Conclusions. These results provide new observational constraints on the properties of the solar cycle periodicities, the grand minima generation, and thus the long-term behavior of the solar dynamo.

Sparse component analysis‐based under‐determined blind source separation for bearing fault feature extraction in wind turbine gearbox

The signal processing-based bearing fault diagnosis in wind turbine gearbox is considered challenging as the vibration signals collected from acceleration transducers are, in general, a mixture of signals originating from an unknown number of sources. Even worse, the source number is often larger than the number of installed sensors, and hence the fault characterisation is effectively an under-determined blind source separation problem. In this study, a novel sparse component analysis-based algorithmic solution is proposed to address this technical challenge from two aspects: source number estimation and source signal recovery, to enable accurate and efficient bearing fault diagnosis. The source number estimation is implemented based on the empirical mode decomposition and singular value decomposition joint approach. The observed signals are transformed to the time-frequency domain using short-time Fourier transform to obtain the sparse representation of the signals. The fuzzy C-means clustering and l 1 norm decomposition methods are used to estimate the mixing matrix and recover the source signals, respectively. The proposed solution is assessed through simulation experiments for scenarios of linearly and non-linearly mixed bearing vibration signals, and the numerical result confirms the effectiveness of the proposed algorithmic solution.

Using Empirical Mode Decomposition to Filter Out Non-turbulent Contributions to Air–Sea Fluxes

A methodology based on Empirical mode decomposition (EMD) was used to filter out non-turbulent motions from measurements of atmospheric turbulence over the sea, aimed at reducing their contribution to eddy-covariance (EC) estimates of turbulent fluxes. The proposed methodology has two main objectives: (1) to provide more robust estimates of the fluxes of momentum, heat and CO\(_2\); and (2) to reduce the number of flux intervals rejected due to non-stationarity criteria when using traditional EC data processing techniques. The method was applied to measurements from a 28-day cruise (HALOCAST 2010) in the Eastern Pacific region. Empirical mode decomposition was applied to 4-h long time series data and used to determine the cospectral gap time scale, \(T_\mathrm{{gap}}\). Intrinsic modes of oscillation with characteristic periods longer than the gap scale due to non-turbulent motions were assumed and filtered out. Turbulent fluxes were then calculated for sub-intervals of length \(T_\mathrm{{gap}}\) from the filtered 4-h time series. In the HALOCAST data, the gap scale was successfully identified in 89% of the 4-h periods and had a mean of 37 s. The EMD approach resulted in the rejection of 11% of the flux intervals, which was much less than the 68% rejected when using standard filtering methods based on data non-stationarity. For momentum and sensible heat fluxes, the averaged difference in flux magnitude between the traditional and EMD approaches was small (3 and 1%, respectively). For the CO\(_2\) flux, the magnitude of EMD flux estimates was on average 16% less than fluxes estimated from linear detrended 10-min time series. These results provide evidence that the EMD method can be used to reduce the effects of non-turbulent correlations from flux estimates.

New method for modal identification of super high-rise building structures using discretized synchrosqueezed wavelet and Hilbert transforms

Summary Measured signals obtained by sensors during dynamic events such as earthquake, wind, and wave contain nonlinear, nonstationary, and noisy properties. In this paper, a new approach is presented for modal parameter identification of structures particularly suitable for very large real-life structures such as super high-rise building structures based on the integration of discretized synchrosqueezed wavelet transform, the Hilbert transform, and the linear least-square fit. Its effectiveness is demonstrated first by application to a two-dimensional frames from the literature, and then to the 123-story Lotte World Tower (LWT) under construction in Seoul, Korea. The LWT measurements are very low-amplitude ambient vibrations. Extracting the natural frequencies and damping ratios from such low-amplitude signals are known to be very challenging. Further, the new methodology was compared with the empirical mode decomposition. It is demonstrated that the new method is capable of extracting both natural frequencies and damping rations from low-amplitude signals effectively and with a higher accuracy compared with the empirical mode decomposition approach. The results of this research indicate a super high-rise building like LWT has a damping ratio in the range 0.7–3.4%. The new method is quite promising for practical implementations of health monitoring of large real-life structures.

Enhanced fault diagnosis of roller bearing elements using a combination of empirical mode decomposition and minimum entropy deconvolution

This paper introduces a new signal processing algorithm for vibration-based fault detection and diagnosis of roller bearings. The methodology proposed in this paper is based on the combination of two data-adaptive techniques which are further enhanced through the use of an automatic feature identification mechanism. The new technique, introduced as empirical mode envelope with minimum entropy, combines elements from the empirical mode decomposition (EMD) and minimum entropy deconvolution (MED) approaches with an energy moment technique to improve the feature selection stage of the EMD algorithm. This improvement allows the processing chain to identify early stage roller bearing faults in noisier signals. The energy moment technique is used to automatically identify the most appropriate intrinsic mode function from the EMD process prior to the MED algorithm being applied. This is in contrast to conventional approaches which tend to use the first mode or make selections based on traditional energy techniques. The combination of the adaptive techniques of EMD and MED allows the development of an improved technique for fault detection and diagnosis of signals. Combining these techniques with the energy moment approach allows further improved fault detection in complex non-stationary conditions. The processing chain has been tested using data obtained during laboratory testing. From the experimental results, it is shown that the new technique is capable of the detection of early stage (minor) roller and outer race defects found in tapered-roller-bearings rotating at a variety of speeds and noise scenarios.

An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organizing Map (SOM) in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to their ability to capture daily discharge at Lock and Dam 10 along the Kentucky River as well as the hybrid design of EEM-SOM-LGP to make predictions multiple time-steps ahead. Different model designs were implemented to demonstrate the improvements of hybrid designs compared to LGP as a standalone application. Additionally, LGP was utilized to gain a better understanding of the catchment in question and to assess its ability to capture different aspects of the flow hydrograph. As a standalone application, LGP was able to outperform published Artificial Neural Network (ANN) results over the same dataset, posting an average absolute relative error (AARE) of 17.118 and Nash-Sutcliff (E) of 0.937. Utilizing EEMD derived IMF runoff subcomponents for forecasting daily discharge resulted in an AARE of 14.232 and E of 0.981. Clustering the EEMD-derived input space through an SOM before LGP application returned the strongest results, posting an AARE of 10.122 and E of 0.987. Applying LGP to the distinctive low and high flow seasons demonstrated a loss in correlation for the low flow season with an under-predictive nature signified by a normalized mean biased error (NMBE) of −2.353. Separating the rising and falling trends of the hydrograph showed that the falling trends were more easily captured with an AARE of 8.511 and E of 0.968 compared to the rising trends AARE of 38.744 and E of 0.948. Utilizing the EEMD-SOM-LGP design to make predictions multiple-time-steps ahead resulted in a AARE of 43.365 and E of 0.902 for predicting streamflow three days ahead. The results demonstrate the effectiveness of utilizing EEMD and an SOM in conjunction with LGP for streamflow forecasting.

Effects of slow and regular breathing exercise on cardiopulmonary coupling and blood pressure.

Investigation of the interaction between cardiovascular variables and respiration provides a quantitative and noninvasive approach to assess the autonomic control of cardiovascular function. The aim of this paper is to investigate the changes of cardiopulmonary coupling (CPC), blood pressure (BP) and pulse transit time (PTT) during a stepwise-paced breathing (SPB) procedure (spontaneous breathing followed by paced breathing at 14, 12.5, 11, 9.5, 8 and 7 breaths per minute, 3min each) and gain insights into the characteristics of slow breathing exercises. RR interval, respiration, BP and PTT are collected during the SPB procedure (48 healthy subjects, 27±6years). CPC is assessed through investigating both the phase and amplitude dynamics between the respiration-induced components from RR interval and respiration by the approach of ensemble empirical mode decomposition. It was found that even though the phase synchronization and amplitude oscillation of CPC were both enhanced by the SPB procedure, phase coupling does not increase monotonically along with the amplitude oscillation during the whole procedure. Meanwhile, BP was reduced significantly by the SPB procedure (SBP: from 122.0±13.4 to 114.2±14.9mmHg, p<0.001, DBP: from 82.2±8.6 to 77.0±9.8mmHg, p<0.001, PTT: from 172.8±20.1 to 176.8±19.2ms, p<0.001). Our results demonstrate that the SPB procedure can reduce BP and lengthen PTT significantly. Compared with amplitude dynamics, phase dynamics is a different marker for CPC analysis in reflecting cardiorespiratory coherence during slow breathing exercise. Our study provides a methodology to practice slow breathing exercise, including the setting of target breathing rate, change of CPC and the importance of regular breathing. The applications and usability of the study results have also been discussed.

Hierarchical order statistics filtering for fast bi-dimensional EMD

A hierarchical approach for fast bi-dimensional empirical mode decomposition (B-EMD) is proposed. The presented approach utilizes an efficient window...

Novel Downsampling Empirical Mode Decomposition Approach for Power Quality Analysis

The development and application of techniques and methodologies for the analysis of power quality (PQ) signals that offer a more efficient and reliable analysis in terms of processing and performance are still issues for industrial and academic fields, mainly considering the quick growing of the PQ data in modern power systems. In this regard, an iterative downsampling stage fused to the empirical mode decomposition (EMD) method is proposed. It is validated and tested using synthetic and real signals. In general, the aim of the proposed method is to extract the fundamental component as the first intrinsic mode function (IMF) to simplify the remaining decomposition. The proposed method is compared to the classical EMD and the ensemble EMD (EEMD). Advantages of the proposed method include a more adequate IMF extraction than the EMD technique, and a noticeable reduction of computational burden compared to the EEMD. The results obtained from the synthetic and real signals demonstrate the reliability and efficiency of the proposed method.

Empirical mode decomposition approach for defect detection in non-stationary thermal wave imaging

This paper introduces a novel empirical mode decomposition based anomaly detection in Quadratic frequency modulated thermal wave imaging. Being suited for non-stationary signal analysis, its edge over other contemporary processing modalities in its anomaly detection capability has been verified using experimentation carried over a mild steel specimen with embedded flat bottom holes. It also addresses the effect of size and depth on anomaly detection using the proposed methodology in addition to considering the signal to noise ratio of defects for detection.

The effect of global activities and warming on oil price

ABSTRACTIn this article, we have attempted to examine the relationships among the crude oil price, global activities index, and global temperature, as well as the exchange rate over the period from January 1982 to December 2009, using the empirical mode decomposition, autoregressive-distributed lag and threshold regression approach. The results indicate that the long-run elasticity of the oil price in relation to global activities is found to be 0.559, which is lower than the finding of He et al. (2010). These results are possibly due to recent improvements in technology that have led to a reduction in demand for crude oil as global activities increase. Furthermore, we have found that global warming will help to reduce the oil price as the global temperature remains below the threshold value. However, after it exceeds the threshold value, as the global temperature continues to rise, it will have no effect in terms of reducing the oil price.

Introducing a combined approach of empirical mode decomposition and PCA methods for maternal and fetal ECG signal processing

Objectives: The purpose of this study was to evaluate the effectiveness of the non-linear adaptive combined approach on the analysis of maternal and fetal signals corrupted by noise.Methods: Empirical mode decomposition (EMD) is a non-linear adaptive technic for data analysis and has been widely used in biomedical data. When we apply EMD on ECG signals, the number of modes that contain cardiac information may vary regarding the subject, type of the signal and recording conditions. This fact can cause some difficulties in signal reconstruction and noise removal using the derived modes. For overcoming this issue, we designed a method to combine principal component analysis (PCA) method with EMD to remove the correlation between the calculated modes and provide a smaller set of uncorrelated orthogonal.Results: We have developed a combined method that proves the power of using PCA on the output of EMD method. The combined method reduces the power of oscillatory artifacts of the baseline. Thus, the PCA-EMD combination provides a noise-free signal.Conclusions: The combination of EMD and PCA methods worked well in being adaptive (from of EMD) and reconstruction (from PCA). It has been proved that this combined method is helpful in separating the signal components, especially in extracting the pure data from the baseline fluctuations.