Optimal Intrinsic Mode Function Research Articles

Application of improved adaptive method of CEEMD and data-drive in fault diagnosis

Purpose The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis. Design/methodology/approach This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis. Findings According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate. Research limitations/implications In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied. Originality/value Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Signal de-noising based on ICEEMDAN for delamination detection by phased array ultrasonic testing

Abstract Aiming at the problem of clutter noise of inter-layer reflections that occurs in multilayer heterogeneous medium such as hybrid fibre composites during ultrasonic inspection, a signal de-noising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed in this paper. In the method, the cross-correlation coefficient is utilized to identify the intrinsic mode function (IMF) that is related to raw ultrasonic signal. Particularly, the root mean square error (RMSE), as selection criterions, are employed conjunctively to sift the optimal IMF among selected decomposition signals. Hilbert transform is used to extract the envelope of the signal after removing the interlayer reflection noise. Experimental results show that, the proposed method can adaptively decompose the non-stationary ultrasonic signal into several IMFs with different spectrum so as to dealing with clutter noises. For delaminate detection by phased array ultrasonic testing for hybrid fibre composites, structural noises are significantly suppressed in the reconstructed signal that retains the distinct echo characteristics of a delaminated defect.

Damage Identification of Simple Supported Bridges Under Moving Loads Based on Variational Mode Decomposition and Deep Learning

Aiming at rapid and economical damage detection of a large number of simple supported bridges, a new structural damage identification method under moving load based on variational mode decomposition (VMD) and deep learning is proposed. Firstly, a moving vehicle is used as an exciting load to invoke structural damage feature and enhance the signal-to-noise ratio, and the structural vertical acceleration response is extracted by a finite element simulating analysis under various damage cases. In order to simulate the influence of noise and expand the samples, Gaussian white noise is added to the extracted data, and then the response signal is decomposed into a series of intrinsic mode functions (IMFs) using VMD, and the optimal IMF component is selected as the damage sample of the structure. Then, a one-dimensional convolutional neural network (CNN) model is built and trained by the various samples of damage. The vibration response of the practical bridge is processed and inputted by the trained CNN model to identify the location of the damage and degree of the structure. Finally, the effectiveness and anti-noise performance of the proposed method are verified through numerical analysis and a simply supported beam bridge model experiment. The results show that the average identification accuracy of the numerical simulations and experimental is 93.4% and 86.8% with 20% Gaussian white noise, respectively. Sensors at different locations have almost the same identification effect for various cases of damage, so it is possible to identify structural damage only using a small amount of accelerometer.

EMDet: An entropy blending and multi-link parallel feature enhancement detection model for gas pipeline weak leakage detection

To improve the detection accuracy of gas pipeline leakage detection (PLD) especially when the leakage is weak, we propose a hybrid deep learning model, named EMDet in this paper, which incorporates two proposed algorithms called entropy blending (EB) and multi-link parallel feature enhancement (MPFE). Firstly, the EB algorithm is used to determine the optimal number of decomposition layer Lo and the optimal intrinsic mode function Io for variational mode decomposition (VMD), elevating the leakage-related information in Io. Then, three coarse-grained scales have been applied to extract effective and robust features, where the coarse-grained scales are calculated based on the sizes required by the MPFE algorithm. Moreover, the MPFE algorithm is proposed to enhance the representation of features, thereby improving the detection accuracy of PLD. After that, the performance of the proposed EMDet has been evaluated on the negative pressure wave (NPW) data collected from realistic urban environments. Compared with the state-of-the-art PLD models, the performance of EMDet not only reaches the total accuracy of 98.67%, the F1-score of 98.65%, and the fault detection rate of 98.68%, but also reduces the false alarm rate to 0.66% and the missing alarm rate to 1.32%. Finally, the robustness results demonstrate the superior performance and broad application prospects of EMDet for PLD.

A hybrid crude oil price forecasting framework: Modified ensemble empirical mode decomposition and hidden Markov regression

AbstractThe considerable influence of crude oil prices on the international economy has motivated numerous scholars to develop various prediction models. Two difficulties are encountered in forecasting. One is that the time series of crude oil prices show massive jumps in high frequency. The other is that the time series of crude oil prices are characterised by nonlinearity, structural breaks and highly volatile states. Therefore, we propose a hybrid model that incorporates the principle of decomposition–integration. Its overall process can be divided into the steps of decomposition and integration. In decomposition step, modified ensemble empirical mode decomposition is used to decompose the data on crude oil prices. Next, K‐means and principal component analysis are utilised to extract the optimal intrinsic mode function (IMF) from diverse frequencies amongst decomposed IMFs. Following decomposition, different forecasting methods are used to match IMFs with various frequencies for prediction. In accordance with structural breaks and the highly volatile states of high‐frequency series, hidden Markov regression is applied to describe predicted values in a probabilistic manner, providing objective interpretation and forecasting. A neural network is taken to predict intermediate‐frequency IMFs by using a fully connected network. Owing to the low frequency and tendency of the residual sequence to be more stable than other frequency sequences, the autoregressive integrated moving average model is applied as the forecasting method for prediction. Finally, all individual predictions for the eventual results are ensembled as final predictions. Results confirm that the proposed model is better than several normal hybrid and single models in terms of prediction accuracy and Diebold–Mariano test results. All results confirm that the newly proposed hybrid model is a promising tool for the analysis and forecasting of crude oil prices.

Strain signal denoising based on adaptive Variation Mode Decomposition (VMD) algorithm

Addressing the problem of vulnerability of the directly measured signal in the field of strain weighing to the high-energy noise of similar frequency bands, an adaptive VMD algorithm is proposed from the perspective of signal separation for the decomposition and denoising of strain signal in the field of strain weighing. In this paper, the adaptive VMD algorithm is used to determine the optimal values of two key parameters, namely, the number of decomposition layers and the penalty factor, to avoid the blindness of parameter selection. The separation results are tested by parameters such as sample entropy, and then the original measurement signal is adaptively decomposed into multiple optimal intrinsic mode function components, and the effective components after extraction are reconstructed into new observation signals. The analysis results of the strain data collected at the weighing site show that the adaptive VMD algorithm can separate and extract the effective strain signal in line with the actual situation from the original strain signal mixed with noise and achieve the purpose of avoiding the interference of high-energy environmental noise with close frequency bands.

Exploring the relationship between pavement surface texture and friction based on Hilbert-Huang Transformation

To provide a potential method for predicting friction coefficients with pavement texture parameters, the Hilbert-Huang Transformation was used to extract relevant parameters from the pavement profile. Eight parameters of the Optimal Intrinsic Mode Function (OIMF) were calculated based on the Hilbert-Huang Transformation. A correlation analysis was conducted between these parameters and friction, taking into account the influence of velocity. Furthermore, a multiple linear regression model was developed to explore friction prediction. The results revealed that the parameter representing the peak density divided by sharpness in the OIMF exhibited the strongest correlation with friction (R2 =0.78), with this correlation being influenced by velocity. The multiple regression model may provide a reliable method for friction prediction (R2 =0.77), and the predicted friction coefficients demonstrated no significant deviation from the measured values (p-value < 0.0001).

High-speed train localization algorithm via cooperative multi-classifier network using distributed heterogeneous signals

Long-distance high-speed train localization based on distributed optical fiber sensors (DOFS) has been a challenging issue due to the large-scale heterogeneous sensor nodes. It requires a competent localization algorithm to be capable of strong generalization and quick response. This paper proposes a cooperative multi-classifier network (CMCN) for locating HSTs based on heterogeneous DOFS signals by adaptive modeling of the local characteristics. The proposed CMCN is composed of adaptive feature extraction, lightweight base classifiers and spatial boostrap aggregating (SBA). First, the heterogeneous signals are adaptively transformed to an optimal intrinsic mode function for extracting the statistical features of base classifiers. The base classifiers are constructed based on dynamic soft-margin support vector machine to model local characteristics without computationally burdensome kernel functions by introducing a dynamic penalty factor. The factor is automatically initialized by evaluating the regional consistency before training. Furthermore, the SBA estimates the location of HSTs based on the local states of nodes. It can cooperate with base classifiers for enhanced accuracy by searching for the interval with maximum regional consistency. Finally, a trial is conducted in a high-speed railway in China in long-term running of 92 days. The results prove feasibility and accuracy of the proposed algorithm.

SO-slope entropy coupled with SVMD: A novel adaptive feature extraction method for ship-radiated noise

Slope entropy (SloEn) has been applied as a powerful nonlinear dynamic tool for signal complexity measurement and is widely used for ship-radiated noise signal (S-RNS) feature extraction. However, the thresholds of SloEn affect the entropy value, which influences the effect of feature extraction. Aimed at addressing the problem, this paper uses the snake optimizer (SO) to optimize SloEn and proposes a new entropy indicator, named SO SloEn (SO-SloEn), and then a novel adaptive S-RNS feature extraction method is put forward in combination with the successive variational mode decomposition (SVMD) and SO-SloEn, which solves the parameter selection problem of variational mode decomposition (VMD). First, SVMD is employed to decompose the S-RNS into several intrinsic mode functions (IMFs); after that, the SO-SloEn of IMFs is calculated to obtain the feature matrix dataset; finally, optimal IMF combinations are obtained through feature selection. The effectiveness of the proposed method is verified by two S-RNS cases, and the results indicate that the recognition rate of the proposed method is always the highest compared with other decomposition algorithms and other entropy indicators under the condition of extracting different numbers of IMFs; moreover, the highest recognition rate can reach more than 92% in both cases.

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Owing to the nonlinearity and nonstationarity of the bearing fault signal, it is difficult to identify fault characteristics under the influence of a strong noise environment. The extraction of early weak fault features is critical for the reliability of bearing operations. Therefore, an urgent problem is reasonable noise reduction and feature enhancement in weak-signal processing. Traditional variational modal decomposition (VMD) and stochastic resonance (SR) are commonly applied to detect weak signals in fault diagnosis. The VMD method can decompose the signal into several intrinsic mode functions (IMFs) to effectively reduce the modal aliasing problem. However, uniform standards for the key parameters of decomposition and the selection of the optimal IMF after decomposition are lacking. Meanwhile, some disadvantages of SR still exist; for example, the interference of multiscale noise may lead to false detection by incorrect selection of high-pass filter parameters, and the system parameters are not adaptive to different signals to achieve the best response output. To better address the weak signal feature enhancement, a novel rolling bearing fault diagnosis method combining adaptive VMD and SR by improved differential search (IDS) optimization is proposed. First, the bearing fault signal is decomposed into several IMFs using the IDS-VMD. Second, the feature information of the fault signal is retained and reconstructed using the correlation kurtosis for sensitive modal extraction. Furthermore, the fault features of the reconstructed signal are effectively enhanced by the variable-step IDS-SR, which can reasonably transfer the noise energy of the input components to the fault characteristic frequency. Finally, the periodic pulse can be observed in the corresponding envelope spectrum. The simulated and experimental data show that the proposed method can not only effectively extract the signal feature information in the actual fault but also realize early weak fault diagnosis of rolling bearings more accurately.

An Intelligent Recognition Method of a Short-Gap Arc in Aviation Cables Based on Feature Weight Enhancement

The cases of loose pin of electrical connectors and broken wire harnesses easily occur in aviation cables due to vibration and thus cause the short-gap arc fault. When the vibration frequency exceeds 30 Hz, the short-gap arc fast burning and extinguishing properties make the changes in time domain and frequency domain of fault signals weak, thus increasing the difficulty in the identification of such a fault. In this article, an arc recognition method based on weight enhancement (WE) and principal component analysis (PCA) is proposed to solve the identification problem of the short-gap arc fault caused by its unapparent frequency-domain characteristics. In the proposed method, the currents under different conditions were measured and analyzed based on the considerations of different vibration conditions and loads. The measured current signals were first decomposed with the empirical modal decomposition (EMD) method, and then, optimal intrinsic mode functions (IMFs) were selected based on the correlation coefficient and kurtosis index to reconstruct the signals with WE algorithm. The time-domain, frequency-domain, and sample entropy characteristics extracted from reconstructed signals were used to establish the 3-D eigenvectors with direct fusion algorithm. Finally, the short-gap arc fault was identified with a principal element analysis and dimension reduction method. The experimental results proved that WE could improve the selection of the optimal direction of PCA dimensional reduction projection and that the short-gap arc fault of aviation cables could be quickly and effectively identified with the proposed method.

A Novel Lower-Limb Coordination Assessment Scheme Using Multi-Scale Nonlinear Coupling Characteristics With sEMG

Motor dysfunction (e.g., incoordination of upper or lower limb) significantly limits the individuals’ ability of daily living, and thus the provisioning of a motion coordination assessment method becomes of vital importance. As a quantitative indicator, inter-muscular coupling strength could assess limb coordination. Since surface electromyography (sEMG) signals cover nonlinear coupling characteristics, in this paper, a lower-limb motion coordination assessment scheme with intermuscular coherence (IMC) analysis and optimization variational mode decomposition (VMD) is proposed. First, sEMG signals are decomposed into several intrinsic mode functions (IMFs) using optimized VMD. Then, the nonlinear coupling feature vector in the beta-band frequency domian (15-35Hz) is extracted by the G-P algorithm from optimal IMF, thereby estimating intermuscular coupling strength by IMC. In particular, differential evolution (DE) algorithm’s global optimization capability and envelope entropy (EE)’s sparsity are adopted to provide the basis for optimization VMD and screening optimal IMF, respectively. Finally, signals are collected from four muscle pairs of the 12 male and 12 female subjects to assess lower-limb motions coordination. Simultaneously, the independent sample T test is leveraged to compare the effects of gender on groups’ characteristics (e.g., age, height, and body mass). Results demonstrate not only the effectiveness of the proposed approach (i.e., p < 0.01 with both “S-walking” and “Running” motions, p < 0.05 with “Q-walking”), but also the difference of motion coordination relationship between male and female adults as well as the significance of muscle selection.

Rotor-Stator Rub-Impact Fault and Position Identification of Aero-Engine Based on VMD-MF-Cepstrum-KNN

In order to precisely estimate rub-impact fault between rotor and stator in aero-engine and identify a rub-impact location, the method combining variation modal decomposition (VMD), margin factor (MF) and cepstrum has been proposed (VMD-MF-Cepstrum). Firstly, to effectively separate rub-impact fault information, VMD algorithm has been recruited to decompose original vibration acceleration signals due to its excellent time-frequency locality and the result provides intrinsic mode function (IMF) from different frequency bands. Furthermore, in view of the sensitivity of margin factor to the wear extent of equipment, it has been used to select the sensitive IMF which can best embody the characteristic information of rub-impact faults and is labelled as optimal IMF. Moreover, considering about the consistency and difference of transfer path of rub-impact fault characteristic information shown by the signals obtained by the sensors which are installed in the different or same position when rubbing position is the same (or different), cepstrum has been taken advantage to depict the feature of transfer path and the transfer path from optimal IMF is extracted. Finally, according to the characteristics which correspond to the transfer-path and the k-nearest neighbor (KNN) algorithm, the fault and locations of rub-impact were judged and identified. Check analysis from 2 independent experiments conducted in 2 days and comparison with classical cepstrum method both indicate that the proposed method will be more ideal for unknown samples. Compared with classical cepstrum method, the correctly identified percentage which corresponds to 2 independent experiments has been raised 52.00% and 43.25%.

Investigation on an in-process chatter detection strategy for micro-milling titanium alloy thin-walled parts and its implementation perspectives

Thin-walled micro parts are widely used in many fields, such as aviation, aerospace, precision engineering, and even micro-molds. Compared with other micro-fabrication technology, micro-milling can be considered as one of the most efficient 3D fabrication techniques. However, due to the vulnerable stiffness of thin-walled micro parts and micro tools, regenerative chatter is prone to occur during the micro-milling process. Severe chatter can lead to large fluctuations in cutting forces, resulting in deterioration of surface roughness/integrity and decreasing in tool life. Therefore, an in-process chatter detection strategy based on the feature extraction of micro-milling forces is proposed in this paper. Micro-milling force signals are measured and processed by advanced algorithms developed. After variational mode decomposition (VMD) for each group of cutting forces, the optimal intrinsic mode function (IMF) is adopted based on the Laplacian score (LS). The multi-scale permutation entropy (MSPE) values of the optimal IMF of each group are calculated, wherein the scale factor s, the embedding dimension m, and the delay time t are obtained by the genetic algorithm (GA). The obtained MSPE values are used as input vectors to train the support vector machine (SVM) classifier, which is used to monitor the stability states of the micro-milling processes. The effectiveness of the proposed strategy is compared with the other classic chatter detection methods and indicators in the micro-milling experiments of thin-walled parts. The results show that the method resulted from the strategy can extract cutting force features effectively and in-process detect chatters accurately in micro-milling thin-walled parts processes.

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.

Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis

The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of insufficient decomposition and modal aliasing due to the unclear selection method of modal component k and penalty factor α. Therefore, it is difficult to ensure the accuracy of fault feature extraction and fault diagnosis. To effectively extract fault feature information from bearing vibration signals, a fault feature extraction method based on VMD optimized with information entropy, and robust independent component analysis (RobustICA) was proposed. Firstly, the modal component k and penalty factor α in VMD were optimized by the principle of minimum information entropy to improve the effect of signal decomposition. Secondly, the optimal parameters weresubstituted into VMD, and several intrinsic mode functions (IMFs) wereobtained by signal decomposition. Secondly, the kurtosis and cross-correlation coefficient criteria were comprehensively used to evaluate the advantages and disadvantages of each IMF.And then, the optimal IMFs were selected to construct the observation signal channel to realize the signal-to-noise separation based on RobustICA. Finally, the envelope demodulation analysis of the denoised signal was carried out to extract the fault characteristic frequency. Through the analysis of bearing simulation signal and actual data, it shows that this method can extract the weak characteristics of rolling bearing fault signal and realize the accurate identification of fault. Meanwhile, in the bearing simulation signal experiment, the results of kurtosis value, cross-correlation coefficient, root mean square error, and mean absolute error are 6.162, 0.681, 0.740, and 0.583, respectively. Compared with other traditional methods, better index evaluation value is obtained.

Unknown fault feature extraction of rolling bearings under variable speed conditions based on statistical complexity measures

As the main transmission components of rotating machinery, rolling bearings have important research significance for fault diagnosis and state detection. However, the operating environment of mechanical equipment is complex, and the fault characteristic information of rolling bearings is often unknown. Through the complex transmission path, the bearing seat vibration sensor picks up the fault vibration response is weaker, and it is often submerged by strong noise. When the speed and load of rotating mechanical equipment change, the fault characteristic of rolling bearings is more obvious under variable speed conditions. The fault diagnosis problem of rolling bearings needs to be solved urgently under strong noise background, variable speed, and unknown fault characteristics. Therefore, this paper studies an unknown fault feature extraction method of variable speed rolling bearing based on statistical complexity measures (SCM). Order analysis preprocesses the variable speed vibration signal of rolling bearings. It is convenient for subsequent fault feature extraction and analysis. The SCM selects the optimal intrinsic mode function (IMF) component corresponding to the Empirical mode decomposition (EMD) decomposition, and it is also evaluated index for the optimal response of stochastic resonance. Therefore, the adaptive frequency shift stochastic resonance effectively extracts the unknown fault features of rolling bearings under strong noise background.

Early Fault Diagnosis of Gearbox Based on VWPSO-VMD

Abstract Aiming at the problem that the characteristic of the early weak fault of the gearbox is submerged by the background noise under the low-speed operating condition, this paper proposes a gearbox fault diagnosis method based on the variable weighted particle swarm optimization variational mode decomposition (VWPSO-VMD). First, a variable weight particle swarm optimization algorithm (VWPSO) is proposed, and the effectiveness of the VWPSO is verified by the optimization calculation of test functions. Secondly, using fuzzy entropy as the fitness function, the proposed VWPSO is used to optimize the modal number K and the penalty factor α of the variational mode decomposition (VMD). Then, based on the grey relational analysis method (GRA), the optimal intrinsic mode function (IMF) is selected for envelope demodulation analysis. Finally, VWPSO-VMD and empirical mode decomposition (EMD) are used to diagnose the early crack and broken tooth fault signals of the gearbox. The results show that, compared with EMD, VWPSO-VMD can more accurately extract the early fault characteristic frequency of the gearbox. This provides a new method for the extraction of early fault features of the gearbox.

Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy.

The complex and harsh working environment of rolling bearings cause the fault characteristics in vibration signal contaminated by the noise, which make fault diagnosis difficult. In this paper, a feature enhancement method of rolling bearing signal based on variational mode decomposition with K determined adaptively (K-adaptive VMD), and radial based function fuzzy entropy (RBF-FuzzyEn), is proposed. Firstly, a phenomenon called abnormal decline of center frequency (ADCF) is defined in order to determine the parameter K of VMD adaptively. Then, the raw signal is separated into K intrinsic mode functions (IMFs). A coefficient En for selecting optimal IMFs is calculated based on the center frequency bands (CFBs) of all IMFs and frequency spectrum for original signal autocorrelation operation. After that, the optimal IMFs of which En are bigger than the threshold are selected to reconstruct signal. Secondly, RBF is introduced as an innovative fuzzy function to enhance the feature discrimination of fuzzy entropy between bearings in different states. A specific way for determination of parameter r in fuzzy function is also presented. Finally, RBF-FuzzyEn is used to extract features of reconstructed signal. Simulation and experiment results show that K-adaptive VMD can effectively reduce the noise and enhance the fault characteristics; RBF-FuzzyEn has strong feature differentiation, superior noise robustness, and low dependence on data length.

Research on test bench bearing fault diagnosis of improved EEMD based on improved adaptive resonance technology

Ensemble empirical mode decomposition (EEMD) is an adaptive signal decomposition method. The selection of the optimal intrinsic mode function (IMF) and the enhancement of the de-noising ability of EEMD have always been the problems that researchers are attempting to solve. The paper proposes an improved EEMD based on the improved adaptive resonance technology (IART) to resolve the above problems. The main work of this paper is described as: At first, the IART theory is summarized based on the traditional resonance technology. Secondly, a novel method to select the optimal IMF(s) based on the resonance frequency (RF) of IART is put forward. Thirdly, for the determination of the parameters of the band-pass filter, an optimization method IART-based is presented, and the parameters are optimized by the RF and the principle of the maximum of envelope kurtosis (MEK) to solve the problem that the center frequency and the bandwidth of the traditional resonance technology determined by experience. Finally, the IART is used as a supplement to EEMD to enhance its de-noising ability. Experimental results on simulated signal and vibration signals measured from rolling bearings have revealed that the improved EEMD can obtain a more satisfactory effect than other commonly used methods.