Improved Empirical Mode Decomposition Research Articles

Real-time prediction of ship motion based on improved empirical mode composition and dynamic residual neural network

Ship motion attitude data have strong random and non-stationary characteristics under severe sea states. Real-time and accurate prediction of the ship's motion attitude can significantly improve the safety of navigation. Therefore, this article proposes a prediction model based on the improved empirical mode decomposition (IEMD) and the dynamic residual recurrent neural network with bidirectional structure and time pattern attention mechanism (TPA-Bi-DRRNN), and proposes a new algorithm: dynamic adaptive beetle swarm antennae search (DABSAS) algorithm to optimise the initial weight and threshold of the prediction model. The input data are adaptively composed into multiple intrinsic mode functions (IMFs) containing their frequency characteristics using IEMD, and a prediction is made for each IMF using TPA-Bi-DRRNN. The model structure can be regulated in real time according to the sliding window. The destroyer DTMB5415 is used as an example to conduct a performance test of the hybrid prediction model and DABSAS. The results show that no mode mixing occurred in the IMFs, the noise in each IMF was significantly reduced, thus dramatically reducing the difficulty of using the input data for prediction; and the optimization performance of DABSAS in applying the TPA-Bi-DRRNN is much better than that of the traditional algorithms. Compared with other models, the difference in the predictive accuracy of TPA-Bi-DRRNN under each condition is the smallest, suggestings that it has extremely high robustness and will always be able to maintain much higher accuracy than other models over a long period, thus meeting the needs for real-time accurate prediction of ship motion attitude.

Study on small leaks in submarine liquid pipelines based on passive acoustic internal detection method

ABSTRACT In a submarine liquid pipeline, the timely detection of tiny amounts of leakage (less than 1 L/min) can have a critical impact on pipeline safety. Hence, a method based on a passive acoustic internal detection for pipeline small leakages was established. Firstly, the variation laws between leakage rates and sound power levels with different leakage apertures and internal pressures were simulated using Ansys Fluent 19.0 software. Furthermore, an experimental platform for pipeline small leakage detection was constructed. The acoustic experiments of small leakage detection were carried out under different leakage apertures and internal pressures using a high-sensitivity acoustic sensor placed into the pipeline. It was found that consistency between the experimental results and the finite element simulation results was established. A novel improved empirical mode decomposition (IEMD) was used to denoise the leakage signals. The characteristic parameters of different signal processing fields were extracted. A novel decision tree support vector machine (DTSVM), based on parameter optimization, was used to construct a classification model to identify small leakages through different apertures. The results show that compared with three commonly used models, the accuracy of this novel model of pipeline small leakage detection was 92.2%. This model was found to improve the small leakages detection rate of different apertures and also provided a theoretical basis for developing an acoustic internal detector for submarine liquid pipelines.

Bolt-Loosening Identification by Using Empirical Mode Decomposition and Sample Entropy

In bolted connection structures, some nonlinear effects take place when the ultrasonic wave travels through the contact area of the parts, and this contact nonlinear characteristics is influenced by the preload of bolts. In this study, an improved empirical mode decomposition (IEMD) algorithm is developed to extract the intrinsic mode functions (IMFs) related to the preload-dependent nonlinear characteristics, and then, these ones are reconstructed. Finally, the normalized sample entropy of the new signal is employed for bolt preload and loosening state detection. To validate this new method, an experimental setup with four specimens is designed and fabricated. The experimental results of three specimens show that the normalized entropy decreases monotonously from 1.35 to 1 as the tightening torque increases from 5 to 20 Nm. Moreover, Pearson’s coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> of the fit lines is greater than 0.99, and the relative errors of the slope and intercept of the fit lines are no more than 3.0%. After this linear relationship is obtained, the tightening torque of the fourth specimen is estimated, and the relative error between the estimated and actual torque of specimen is 0.8%–7%, which demonstrates that this proposed method can detect the tightening torque accurately. In addition, compared to the traditional energy-based index, the saturation phenomenon is not observed in the entropy when the torque is near to the rated one, which indicates that the new approach can identify the early loosening state of bolts and its identification performance is much more efficient.

A novel hybrid feature extraction approach of marine vessel signal via improved empirical mode decomposition and measuring complexity

The feature extraction of marine vessel-radiated noise (MVRN) under the complex ocean background is explored. To this end, a hybrid approach is presented based on the analysis of MVRN in subspaces of intrinsic mode functions (IMF) extracted using the improved empirical mode decomposition (IEMD) and measuring complexity. The restriction of the end-effect is an important problem when employing the EMD algorithm. In this study, first, to reduce the end-effects, an IEMD algorithm based on the correlation expansion model is proposed. Then, a comparative study of IEMD, classic EMD (CEMD), and EMD by other expansion methods are conducted on several signals. Next, IEMD, CEMD, and variational mode decomposition (VMD) algorithms are utilized to extract a group of IMFs for three types of MVRN. Later, one obtained IMF from each method that contains the most dominant information is selected. Lastly, two statistical complexity measures (i.e., permutation entropy (PE) and slope entropy (SlopEn)) are used as the features of the chosen IMF to improve the underwater signal separability and stability. Experimental results indicate that the suggested approach (IEMD-PE/SlopEn) can effectively extract the feature information of underwater signals. Additionally, it has a better ability to discriminate between various kinds of MVRN.

Predicting Multidimensional Environmental Factor Trends in Greenhouse Microclimates Using a Hybrid Ensemble Approach

Trend prediction of greenhouse microclimate is crucial, as greenhouse crops are vulnerable to potential losses resulting from dramatic changes in greenhouse microclimate. Consequently, a precise greenhouse microclimate predictive model is required that can predict trends in greenhouse microclimates several weeks in advance to avoid financial losses. In the present study, we proposed a hybrid ensemble approach to predict greenhouse microclimate based on an Informer model that is optimized using improved empirical mode decomposition (IEMD). The dataset was decomposed using IEMD, and then all the decomposed datasets were predicted using the Informer model. Afterward, the predictions were combined. In the present study, five different environmental factor datasets of CO2 concentration, atmospheric pressure, light intensity, temperature, and humidity were predicted. The performance of the IEMD‐Informer model was compared with other modeling approaches. The results demonstrate that the proposed method has outstanding performance and can predict the greenhouse microclimate environmental factors more accurately.

A strategy for predicting waste production and planning recycling paths in e-logistics based on improved EMD-LSTM.

With the rapid development of e-commerce, express delivery has been chosen and accepted by consumers, and a large number of express packages have resulted in serious waste of resources and environmental pollution. Because of the irregularity of online goods purchases by users in real life, logistics parks are unable to accurately judge the recycling needs of various regions. In order to solve this problem, we propose an improved empirical mode decomposition (IEMD) algorithm combined with a long-short-term memory (LSTM) network to deal with the addresses and categories in logistics data, analyze the distribution of recyclable logistics waste in the logistics park service area and in the express recycling station within the logistics park, judge the value of recyclable logistics waste, optimize the best path for recycling vehicles and improve the success rate of logistics waste recycling. In order to better research and verify the IEMD-LSTM prediction model, we model and simulate the algorithm behavior of the express waste packaging recycling prediction model system, and compare it with other classification methods through specific logistics data experiments. The prediction accuracy, stability and advantages of the four algorithms are analyzed and compared, and the application reliability of the algorithm proposed in this paper to the logistics waste recycling process is verified. The application in the actual express logistics packaging recycling case shows the feasibility and effectiveness of the waste recycling scheme proposed in this paper.

A Hybrid Method for Structural Modal Parameter Identification Based on IEMD/ARMA: A Numerical Study and Experimental Model Validation

This article presents a hybrid method of structural modal parameter identification, based on improved empirical mode decomposition (EMD) and autoregressive and moving average (ARMA). Special attention is given to some implementation issues, such as the modal mixing, false modes, the judgment of the real intrinsic mode function (IMF) of classical EMD, and the difficulty of fixing the order of ARMA. To resolve the existing defects of EMD, an improved EMD (IEMD) that combines frequency band filtering and cluster analysis is proposed in this paper, where frequency band filtering divides the signal into several narrowband signals before the EMD process, and cluster analysis is used to determine the real IMFs. Euclidean distance is used to cluster the decomposition results, with no need to adjust any indexes or thresholds, and only by means of using the nearest distance to efficiently determine the real IMF. Moreover, IEMD is used as a pre-processing tool for ARMA, to resolve the difficulty of fixing its order. The capabilities of the proposed method were compared and assessed using a numerical simulation and an experimental model. The numerical simulation and experimental results showed that the improved method could resolve the modal mixing and false modal problems in the classical EMD process and could automatically identified the real IMFs, while the proposed IEMD was combined with ARMA to successfully identify the frequency and mode shape of the structure. Additionally, since each IMF is a single component signal, it is easy to determine the order of the ARMA model.

Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition

Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

Geomagnetic signal de-noising method based on improved empirical mode decomposition and morphological filtering

To solve the problem that geomagnetic signals are susceptible to random noise and instantaneous pulse interference in geomagnetic navigation, a geomagnetic signal de-noising method based on improved empirical mode decomposition (IEMD) and morphological filtering (MF) is proposed. The instantaneous pulse interference is eliminated by designing different structural elements according to the characteristics of the pulse signal. The signal after filtering the instantaneous pulse interference is decomposed by EMD, and the intrinsic mode functions (IMFs) obtained from the decomposition are determined as two modes (i.e. noise IMFs and mixed IMFs) by the cross-correlation coefficient criterion. The noise IMFs are removed directly, and a normalized least means square filter (NLMS) is designed to remove noise from mixed IMFs, which can adaptively adjust the filtering parameters according to the noise level of different IMF components. The noise-reduced mixed IMFs and residual are reconstructed to obtain the final geomagnetic signal. Experiment results illustrate that the proposed MF-IEMD method can effectively achieve noise reduction. Comparing with the traditional EMD and MF-EMD de-noising methods, the root mean square errors(RMSE) decreased by 49.27% and 24.79%, respectively.

An Improved Empirical Mode Decomposition Based on Adaptive Weighted Rational Quartic Spline for Rolling Bearing Fault Diagnosis

As a powerful time-frequency signal analysis technique, empirical mode decomposition (EMD) has been commonly applied in fault diagnosis. However, the cubic spline-curve often causes outstanding over and undershoot problem, which significantly limits the performance of conventional EMD. To address this problem, an improved EMD (I-EMD) based on adaptive weighted rational quartic spline is proposed. Firstly, the original cubic spline interpolation in conventional EMD is replaced with the weighted rational quartic spline interpolation (WRQSI) which has two adjustable shape control parameters. Secondly, a novel parameter selection criterion termed envelope characteristic frequency ratio (ECFR) is designed to guide the construction of the optimal local envelope. And simulation analysis proved that ECFR is not only sensitive to the characteristic information but also robust to the weak noise and sudden impulses. Subsequently, the optimal shape control parameter can be searched by grasshopper optimization algorithm (GOA) using the maximum ECFR as the objective function. Then the sensitive modes selected via weighted Kurtosis index are employed for further Hilbert envelope spectrum analysis. Finally, two case studies on rolling bearing fault diagnosis are constructed to verify the rationality and effectiveness of the I-EMD method. The results show that I-EMD method can evidently solve the over and undershoot problem and restrain the mode mixing phenomenon. Moreover, I-EMD also performs better fault feature extraction ability under the same conditions compared with EMD, VMD and CEEMDAN. So it is expected that I-EMD will serve as a potential improvement for signal processing, fault feature extraction and fault diagnosis.

A New Hybrid Model for Short-Term Electricity Load Forecasting

Nowadays electricity load forecasting is important to further minimize the cost of day-ahead energy market. Load forecasting can help utility operators for the efficient management of a demand response program. Forecasting of electricity load demand with higher accuracy and efficiency can help utility operators to design reasonable operational planning of generation units. But solving the problem of load forecasting is a challenging task since electricity load is affected by previous history load, several exogenous external factors (i.e., weather variables, social variables, working day or holiday), time of day, and season of the year. To solve the problem of short-term load forecasting (STLF) and further improve the forecasting accuracy, in this paper we have proposed a novel hybrid STLF model with a new signal decomposition and correlation analysis technique. To this end, load demand time series is decomposed into some regular low frequency components using improved empirical mode decomposition (IEMD). To compensate for the information loss during signal decomposition, we have incorporated the effect of exogenous variables by performing correlation analysis using T-Copula. From the T-Copula analysis, peak load indicative binary variable is derived from value at risk (VaR) to improve the load forecasting accuracy during peak time. The data obtained from IEMD and T-Copula is applied to deep belief network for predicting the future load demand of specific time. The proposed data driven method is validated on real time data from the Australia and the United States of America. The performance of proposed load forecasting model is evaluated in terms of mean absolute percentage error (MAPE) & root mean square error (RMSE). Simulation results verify that, the proposed model provides a significant decrease in MAPE and RMSE values compared to traditional empirical mode decomposition based electricity load forecasting.

Forecasting day-ahead electricity prices using a new integrated model

Electricity price forecasting proves useful for power producers and consumers to make proper decisions in a market-oriented environment. However, due to the complex drivers and sharp fluctuation of electricity prices, accurate electricity price forecasting turns to be very difficult. To better capture the characteristics of day-ahead electricity prices, a new integrated model based on the improved empirical mode decomposition (IEMD), autoregressive moving average with exogenous terms (ARMAX), exponential generalized autoregressive conditional heteroscedasticity (EGARCH) and adaptive network-based fuzzy inference system (ANFIS) is proposed in this paper. Then it is validated by using the data from Spanish and Australian electricity markets. The results indicate that the forecasting accuracy of the new integrated model proves higher than that of some well-recognized models in the literature.

A new solar power output prediction based on hybrid forecast engine and decomposition model

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method.

Short term electricity load forecasting using a hybrid model

Short term electricity load forecasting is one of the most important issue for all market participants. Short term electricity load is affected by natural and social factors, which makes load forecasting more difficult. To improve the forecasting accuracy, a new hybrid model based on improved empirical mode decomposition (IEMD), autoregressive integrated moving average (ARIMA) and wavelet neural network (WNN) optimized by fruit fly optimization algorithm (FOA) is proposed and compared with some other models. Simulation results illustrate that the proposed model performs well in electricity load forecasting than other comparison models.

Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features

The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved.

Misalignment Fault Diagnosis of DFWT Based on IEMD Energy Entropy and PSO-SVM

Misalignment is an important cause for the early failure of large doubly-fed wind turbines (DFWT). For the non-stationary characteristics of the signals in the transmission system of DFWT and the reality that it is difficult to obtain a large number of fault samples, Solidworks and Adams are used to simulate the different operating conditions of the transmission system of the DFWT to obtain the corresponding characteristic signals. Improved empirical mode decomposition (IEMD), which improves the end effects of empirical mode decomposition (EMD) is used to decompose the signals to get intrinsic mode function (IMF), and the IEMD energy entropy reflecting the working state are extracted as the inputs of the support vector machine (SVM). Particle swarm optimization (PSO) is used to optimize the parameters of SVM to improve the classification performance. The results show that the proposed method can effectively and accurately identify the types of misalignment of the DFWT.

An efficient method for analysis of EMG signals using improved empirical mode decomposition

In this paper, a simple technique with improved empirical mode decomposition (IEMD) in conjunction with four different features is used for the analysis of amyotrophic lateral sclerosis (ALS) and normal EMG signals. EMG signals consist of noise from various sources, such as electronic instruments, moving artifacts and electrical instruments. The empirical mode decomposition (EMD) method followed by median filter (MF) has been employed to remove the impulsive noise from intrinsic mode function (IMF) components generated through EMD. The filtered IMF components are summed together to generate a new signal. EMD process is further applied to new EMG signal to generate improved IMFs called as improved EMD method. In the IEMD algorithm for the first time, a new technique is proposed to choose the window size of median filter. For this, the features namely amplitude modulation bandwidth (BAM), frequency modulation bandwidth (BFM), spectral moment of power spectral density (SMPSD), and first derivative of instantaneous frequency (MFDIF) extracted from the improved IMFs are used to discriminate between ALS and normal EMG signals. Finally, it is observed that IEMD method increases the discrimination ability of these features as compared to the EMD method and the adaptively fast ensemble empirical mode decomposition (AFEEMD) method.

Mixed Pixel Wise Characterization Based on HDP-HMM and Hyperspectral Image Shape Detection using Hybrid Canny Edge Detection and WPDF

Hyper Spectral Imaging (HSI) gathers and processes information from across the electromagnetic spectrum. The information enclosed in hyperspectral data permits the characterization, recognition and classification of the land-covers with enhanced accuracy and robustness. On the other hand, quite a lot of vital complications must be considered during the classification process of hyperspectral data, among which the maximum quantity of spectral channels, the spatial unevenness of the spectral signature, shape discovery of the images and the value of data. Above all, the maximum quantity of spectral channels and low number of labeled training samples pose the setback of the curse of dimensionality and, accordingly, result in the possibility of overfitting the training data. With the aim of solving all these complications, in this study presented the framework of Support Vector Machine (SVM) together with Fuzzy Sigmoid Kernel Function (SVM-FSK) in the circumstance of HSI classification and analyzing their features in the hyperspectral domain. A Kernel Fisher Discriminant Analysis (KFDA) model is employed for the purpose of dimensionality reduction of HSI. The KFDA dimensionality reduction scheme depends on the selection of the kernel in a higher-dimensional HSI feature space. In order to enhance the gradient level of spatial information, employed Improved Empirical Mode Decomposition (IEMD) with Gaussian Firefly Algorithm (GFA) (IEMD-GFA) to boost the mixed pixel wise SVM-FSK classification accuracy. During the process of IEMD scheme, the identifiable of Intrinsic Mode Functions (IMFs) of spectral band, weight values of IMFs are computed with the help of GFA. In order to identify the shape of HSI, novel hybrid scheme depending on the canny operator and fuzzy entropy theory is formulated. This scheme computes the fuzzy entropy of gradients from an image to make a decision on the threshold for the canny operator. For the purpose of detecting the edges and to discover the shape of the object Weibull Probability Density Function (WPDF) scheme is used. The obtained both spectral and spatial pixels are classified using SVM-FSK and estimated by using Hierarchical Dirichlet Process (HDP)-Hidden Markov Model (HMM). The proposed SVM-FSK is assessed with hyperspectral AVIRIS Indian Pine dataset. It shows that the proposed dimensionality reduction with SVM-FSK classification shows improved classification accuracy in terms of parameters like overall accuracy, standard deviation and mean.

Dynamic Detection of Vehicle Motion Stability Based on Time-varying Damping Ratio

In order to dynamically detect vehicle motion stability during operation, as a new method of detecting vehicle motion stability, the time-varying damping ratio, is presented on the basis of the analysis of the dynamic response feature and the non-stationary characteristics of the response signals. The lateral vibration accelerations are decomposed by the IEMD(improved empirical mode decomposition) into a finite and often small number of IMF(intrinsic mode functions). The instantaneous frequency and the instantaneous amplitude of the intrinsic mode components are drawn through normalized Hilbert transform, respectively. The time-varying damping ratios under different intrinsic modes are computed by applying instantaneous amplitude and instantaneous frequency to judge the running stability of the vehicle. The zero or negative damping ratio indicates that the vehicle is unstable, the positive damping ratio shows that the vehicle keeps stable, the absolute value of the negative ratio means the severity of vehicle instability. Through the theoretical and experimental verification applying simulation and the measured signals, the results show the method can accurately and efficiently dynamically capture the time-varying characteristics of the system stability, with a powerful method for transit monitoring of vehicle running stability.

Iris recognition with an improved empirical mode decomposition method

With the increasing need for security systems, iris recognition is one of the reliable solutions for biometrics-based identification systems. In general, an iris recognition algorithm includes four basic modules: image quality assessment, preprocessing, feature extraction, and matching. This work presents a whole iris recognition system, but particularly focuses on the image quality assessment and proposes an iris recognition scheme with an improved empirical mode decomposition (EMD) method. First, we assess the quality of each image in the input sequence and select clear enough iris images for the succeeding recognition processes. Then, an improved EMD (IEMD), a multiresolution decomposition technique, is applied to the iris pattern extraction. To verify the efficacy of the proposed approach, experiments are conducted on the public and freely available iris images from the CASIA and UBIRIS databases; three different similarity measures are used to evaluate the outcomes. The results show that the presented schemas achieve promising performance by those three measures. Therefore, the proposed method is feasible for iris recognition and IEMD is suitable for iris feature extraction.