Empirical Mode Decomposition Energy Entropy Research Articles

Predicting Screening Efficiency of Probability Screens Using KPCA‐GRNN with WP‐EE Feature Reconstruction

The screening system is a nonlinear and non‐Gaussian complex system. To better characterize its attributes and improve the prediction accuracy of screening efficiency, this study involves the acquisition of the vibration signals and screening efficiency data under various operational conditions. Subsequently, empirical mode decomposition energy entropy (EMD‐EE), variational mode decomposition energy entropy (VMD‐EE), and wavelet packet energy entropy (WP‐EE) features are extracted from the time series vibration signals, and three single input energy entropy‐generalized regressive neural network (GRNN) prediction accuracy models are established and compared. Furthermore, we introduce the kernel principal component analysis (KPCA)‐WP‐EE feature reconstruction‐GRNN prediction algorithm. This approach involves reconstructing the feature vector by optimizing WP‐EE‐GRNN prediction under varying parameters. The parameterized GRNN model is then predicted and analyzed through secondary reconstruction involving KPCA dimensionality reduction features. The results show that WP‐EE‐GRNN achieves superior prediction accuracy compared to box dimension (d)‐GRNN, box dimension‐back propagation neural network (BPNN), and d‐weighted least squares support vector machine, WP‐d‐GRNN, WP‐EE‐BPNN, EMD‐EE‐GRNN, and VMD‐EE‐GRNN. Additionally, the WP‐EE feature reconstruction‐GRNN algorithm exhibits higher prediction accuracy than the single‐input WP‐EE‐GRNN algorithm. The WP‐EE‐GRNN prediction algorithm using KPCA dimensionality reduction and secondary reconstruction not only achieves higher prediction accuracy than prior to KPCA dimensionality reduction but also improves prediction efficiency. Following the extraction of two core principal components, model parameters when KPCA’s σ2 = 0.85, the optimal parameter of GRNN model Spread = 0.051, and the optimal number of training samples N = 19, the average prediction error is 1.434%, the minimum prediction error reaching 0.708%, the minimum root mean square error reaching 0.836% and Pearson correlation coefficient marking the closest to 1, these result all representing the optimum achievable values. The budget model selects the optimal parameter combination scheme for the system.

AN EMD AND IMF ENERGY ENTROPY-BASED OPTIMIZED FEATURE EXTRACTION AND CLASSIFICATION SCHEME FOR SINGLE TRIAL EEG SIGNAL

For the purpose of improving the classification accuracy of single-trial electroencephalogram (EEG) signal during motor imagery (MI) process, this study proposed a classification method which combines intrinsic mode functions (IMFs) energy entropy and improved empirical mode decomposition (EMD) scheme. Singular value decomposition (SVD), Gaussian mixture model (GMM), EMD and IMF energy entropy were employed for the newly designed scheme. After removing noise and artifacts from acquired EEG signals in EEGLAB, SVD was applied, and the singular values were clustered by GMM. The insignificant characteristics indicated by the small SVD values were then removed, and the signals were reconstructed, feeding to EMD algorithm. Those IMFs mapping to [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] frequencies were selected as the major features of the Electroencephalogram signal. The SVM classifier with Radial Basis Function Neural Network, linear, and polynomial kernel functions and voting mechanism then kicked in for classification. The results were compared with that of the traditional EMD and EEMD through simulation, showing that the proposed scheme can eliminate mode mixing effectively and improve the single-trial EEG signal classification accuracy significantly, suggesting the probability of designing a more efficient EEG control system based on the proposed scheme.

Open-Circuit Fault Diagnosis of Six-Phase Permanent Magnet Synchronous Motor Drive System Based on Empirical Mode Decomposition Energy Entropy

This paper proposes a method to diagnose the open-circuit faulty phases and faulty points of the six-phase permanent magnet synchronous motor (PMSM) drive circuit. The current sensor is used to obtain the six-phase current signal, and the least mean square error (LMS) adaptive filtering algorithm is used to filter out the vibration and noise. Empirical Mode Decomposition (EMD) is performed on the filtered current signals, and the EMD energy entropy of each phase current signal is calculated. The change of energy entropy can simplify the double-bridge arm open-circuit fault to the single-bridge arm open-circuit fault, which reduces the number of fault characteristics. After the faulty phase is judged by the change of energy entropy, the fault diagnosis system can diagnose the specific faulty point according to the normalized average value of each phase current signal. Finally, the current data of normal state and fault state are used to train the support vector machine (SVM) and classify the fault state. Each type of fault can be accurately diagnosed by substituting experimental data into the SVM. Experimental results prove that the proposed method can accurately diagnose the open-circuit faults of the six-phase PMSM drive system.

Analysis of complex time series based on EMD energy entropy plane

Empirical mode decomposition (EMD) is a self-adaptive signal processing method that can be applied to nonlinear and non-stationary processes perfectly. In view of this good ability of EMD, in this paper, we propose a new method—EMD energy entropy plane—which combines two different tools—EMD energy entropy and complexity-entropy causality plane—to analyze time series. Firstly, we apply EMD energy entropy plane to synthetic data, such as logistic map, Henon map, ARFIMA model and so on, finding that the EMD energy entropy plane presents different trends and distributions when the map is in periodic cycles and chaos. Then we demonstrate the application of EMD energy entropy plane in stock markets. Results show that it is an effective tool of distinguishing two kinds of financial markets. In addition, the introduction of multi-scale reveals the variation law of EMD energy entropy plane at different scales.

Lesion localization algorithm of high-frequency epileptic signal based on Teager energy operator

Abstract High-frequency oscillations reflect the abnormal brain electrical activity in patients with epilepsy. It is significant to research the relationship between high-frequency oscillation and epilepsy originating area for the diagnosis and treatment of epilepsy. In view of the identification of epileptic EEG and the location of epileptic foci, a localization algorithm based on Teager operator is proposed. Firstly, the original epileptic EEG data are preprocessed, the wavelet weighted threshold and the frequency notch method are used to denoise the original epileptic high frequency oscillation signal. Taking into account the frequency characteristics of the high frequency oscillating signal itself, the FIR (Finite Impulse Response) digital filter is used to filter the epileptic high-frequency oscillation signal. Secondly, because the high frequency oscillation rhythm has the characteristics of high frequency, high energy and low amplitude, the Teager energy operator and curve length method are used to extract the characteristics. The PSD (Power Spectral Density) method is applied to qualitative analysis of epileptic lesion location. Finally, the EMD (Empirical Mode Decomposition) algorithm is used to decompose the high-frequency oscillation signal. Combined with Teager energy operator, Teager-huang transform is used to analyze the signal by time-frequency energy analysis. Quantitative analysis of epileptic lesion location is made by using EMD energy entropy method for different lead epileptic signals. The algorithm can effectively locate the location of the focus, independent of individual parameters and high degree of automation. It has good clinical application prospects.

Fractional empirical mode decomposition energy entropy based on segmentation and its application to the electrocardiograph signal

The generalized fractional entropy is a powerful tool for allowing an high sensitivity to the signal evolution, which is available to depict the dynamics of complex systems. Besides, empirical mode decomposition (EMD) energy entropy has received largely extensive attention, such as the field of roller bearing fault diagnosis. In this paper, we change the perspective of research and propose an improved EMD energy entropy built on the theory of the generalized fractional entropy, that is, fractional EMD energy entropy. Furthermore, we also combine the proposed method with new multi-scale algorithm based on the segmentation ideas. In order to show the advantages of this particular method for detecting the complexity of systems, several simulated time series and electrocardiograph (ECG) signal experiments are chosen to examine the performance of them. Through experiments, we find that the new fractional EMD energy entropy shows a better characteristic in the complexity analysis of dynamical systems compared with the classical EMD energy entropy. Moreover, the results reveal that tuning the fractional order allows a higher sensitivity to the series fluctuation. In addition, when the experiment bonds with the multi-scale algorithm based on segmentation, we can obtain more abundant complexity properties of time series through fractional EMD energy entropy. Also, it can effectively discriminate the differences in the complexity of different time series, whereas the original method does not have such good advantages. Especially when applied to ECG signals, this new method can be more effective to distinguish between healthy people and patients with heart disease, which is a valuable advantage. Beyond all that, results of the test on surrogate data generated by randomizing series also further strengthen our summing-up.

Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM

In order to effectively recognize the bearing’s running state, a new method based on kernel principal component analysis (KPCA) and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. First, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. Therefore, the nonlinear feature extraction method KPCA was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model, and the bearing’s running state identification was thereby realized. Cases of test and actual were analyzed. The results validate the effectiveness of the proposed algorithm.

A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA) to extract the characteristic features and the Morlet kernel support vector machine (MSVM) to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker

Based on empirical mode decomposition (EMD) method and support vector machine (SVM), a new method for the fault diagnosis of high voltage circuit breaker (CB) is proposed. The feature extraction method based on improved EMD energy entropy is detailedly analyzed and SVM is employed as a classifier. Radial basis function (RBF) is adopted as the kernel function of SVM and its kernel parameter γ and penalty parameter C must be carefully predetermined in establishing an efficient SVM model. Therefore, the purpose of this study is to develop a genetic algorithm-based SVM (GA-SVM) model that can determine the optimal parameters of SVM with the highest accuracy and generalization ability. The classification accuracy of this GA-SVM approach is tried by real dataset and compared with the SVM, which has randomly selected kernel function parameters. The experimental results indicate that the classification accuracy of this GA-SVM approach is more superior than that of the artificial neural network and the SVM which has constant and manually extracted parameters.

An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine

AbstractTargeting the characteristics of machinery vibration signals of high voltage circuit breaker (CB), a new method based on improved empirical mode decomposition (EMD) energy entropy and multi-class support vector machine (MSVM) to diagnose fault for high voltage CB is proposed. In the fault diagnosis for the high voltage CB, the feature extraction based on improved EMD energy entropy is detailedly analyzed. A new multi-layered classification of SVM named ‘one against others’ algorithm approach is proposed and applied to machinery fault diagnosis of high voltage CB. The extracted features are applied to MSVM for estimating fault type. Compared with back-propagation network (BPN), the test results of MSVM demonstrate that the applying of improved EMD energy entropy to vibration signals is superior to that based on wavelet packet analysis (WPT) and hence estimating fault type on machinery condition of high voltage CB accurately and quickly.

A roller bearing fault diagnosis method based on EMD energy entropy and ANN

According to the non-stationary characteristics of roller bearing fault vibration signals, a roller bearing fault diagnosis method based on empirical mode decomposition (EMD) energy entropy is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs), then the concept of EMD energy entropy is proposed. The analysis results from EMD energy entropy of different vibration signals show that the energy of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to identify roller bearing fault patterns, energy feature extracted from a number of IMFs that contained the most dominant fault information could serve as input vectors of artificial neural network. The analysis results from roller bearing signals with inner-race and out-race faults show that the diagnosis approach based on neural network by using EMD to extract the energy of different frequency bands as features can identify roller bearing fault patterns accurately and effectively and is superior to that based on wavelet packet decomposition and reconstruction.