Adaptive Variational Mode Decomposition Research Articles

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

The intricate nature of compound fault diagnosis in rolling bearings during nonstationary operations poses a challenge. To address this, a novel technique combines adaptive variational mode decomposition (AVMD) with improved multipoint optimal minimum entropy deconvolution adjustment (IMOMEDA). The compound fault signal is isolated through AVMD, with internal parameters obtained via a new indicator termed integrated fault-impact measure index guiding the improved dung beetle optimizer. An adaptive selection method, using a weight factor, chooses the intrinsic mode function containing principal fault data. IMOMEDA whose key parameters are determined by a novel combinatorial strategy is then employed to deconvolute selected fault components, enhancing periodic fault impulses by removing complex interferences and ambient noise. The deconvoluted signal undergoes enhanced envelope spectrum processing to extract fault frequencies and identify fault types. Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for compound faults diagnosis of rolling bearings, showcasing its superiority over existing techniques.

Enhancing long-term river water quality prediction: Construction and validation of an improved hybrid model

To prevent eutrophication of river caused by excessive inputs of nutrients, accurately predicting these nutrient concentrations is of utmost importance. However, long-term accurate predictions of nutrients concentrations remain a challenge. In this study, an improved hybrid algorithm combining adaptive variational mode decomposition and multi-functional recurrent fuzzy neural network (AVMD-MFRFNN) was proposed. Firstly, whale optimization algorithm (WOA) was developed to identify the optimal number of modes for variational mode decomposition (VMD). Then VMD was employed to decompose input data into intrinsic mode fractions (IMFs), benefiting for reducing the noise effect and non-stationarity of data. Then, MFRFNN with the capability of characterizing complex data was developed to train and test the IMFs. The proposed AVMD-MFRFNN achieved impressive results with the root mean square errors (RMSEs) of 0.036 mg/L, 0.758 mg/L and 1.435 mg/L for predicting total phosphorus (TP), chemical oxygen demand (CODMn) and total nitrogen (TN) after 168 hours. These results signify a decrease of 5.3%, 14.1% and 30.1% compared to the second-best compared method. Moreover, this study demonstrates the potential that integrating the proposed AVMD with other methods can enhance the modeling performance of time series. Furthermore, sensitivity analysis results further validate the effectiveness of WOA in optimizing VMD parameter. Finally, the applicability of AVMD-MFRFNN was confirmed by the data from another river section. These results provide new insights and technical support into the management and preservation of river water environment.

Detection and localization of fault in DC microgrid using discrete Teager energy and generalized least square method

PurposeThis paper aims to introduce a new fault protection scheme for microgrid DC networks with ring buses.Design/methodology/approachIt is well recognized that the protection scheme in a DC ring bus microgrid becomes very complicated due to the bidirectional power flow. To provide reliable protection, the differential current signal is decomposed into several basic modes using adaptive variational mode decomposition (VMD). In this method, the mode number and the penalty factor are chosen optimally by using arithmetic optimization algorithm, yielding satisfactory decomposition results than the conventional VMD. Weighted Kurtosis index is used as the measurement index to select the sensitive mode, which is used to evaluate the discrete Teager energy (DTE) that indicates the occurrence of DC faults. For localizing cable faults, the current signals from the two ends are used on a sample-to-sample basis to formulate the state space matrix, which is solved by using generalized least squares approach. The proposed protection method is validated in MATLAB/SIMULINK by considering various test cases.FindingsDTE is used to detect pole-pole and pole-ground fault and other disturbances such as high-impedance faults and series arc faults with a reduced detection time (10 ms) compared to some existing techniques.Originality/valueVerification of this method is performed considering various test cases in MATLAB/SIMULINK platform yielding fast detection timings and accurate fault location.

Health Management of Bearings Using Adaptive Parametric VMD and Flying Squirrel Search Algorithms to Optimize SVM

Bearing, as one of the core parts of rotating machinery, has a running state which is related to the overall operation of the system. Due to the bearing structure and its complex operating environment, running condition monitoring and fault diagnosis is always a key problem in the field of bearing health management, which is of great significance for bearing maintenance and equipment reliability and safety. In view of the difficulty in parameter selection and poor feature extraction ability of variational mode decomposition (VMD) in existing feature extraction, this paper uses the flying squirrel search algorithm (SSA) to optimize the parametric of decomposition layer k and penalty factor α in VMD, and forms an adaptive VMD signal decomposition method. To solve the problem of high dimensionality and long extraction time of multi-domain fault feature set, kernel principal component analysis (KPCA) is used to reduce feature dimensionality. Then, the processed features are input into the support vector machine (SVM) for fault diagnosis and classification, and the parameter optimization ability of SSA is used again to build the SSA-SVM fault diagnosis model. To evaluate the running state of bearings, an alarm threshold method based on the root mean square value calculated by cosine similarity and 3σ is proposed to divide samples of different health states. Finally, the method constructed in this paper is compared with other methods by using simulation and experimental data sets, and the running condition monitoring and fault diagnosis of rolling bearings are successfully realized, which shows the superiority and effectiveness of the method proposed in this paper.

Elimination of heart sound from respiratory sound using adaptive variational mode decomposition for pulmonary diseases diagnosis

Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques.

Wind turbines fault diagnosis method under variable working conditions based on AMVMD and deep discrimination transfer learning network

With the wide application of wind turbines, the bearing fault diagnosis of wind turbines has become a research hotspot. Under complex variable working conditions, the vibration signals of bearing components show non-stationary characteristics. Therefore, it is challenging to extract fault features using typical fault diagnosis methods. This paper proposes Adaptive Multivariate Variational Mode Decomposition combined with an improved Deep Discrimination Transfer Learning Network (AMVMD-IDDTLN) for bearing fault diagnosis of wind turbines under variable working conditions. First, the AMVMD method is used for the adaptive decomposition of the original signal, and use SE-ResNet18 convolutional neural network to obtain the transfer features of the source domain and target domain. Then, marginal distribution differences and conditional differences are assessed by DDM measures. The whole model is optimized by cross-entropy and improved joint distribution adaptation loss function, and the identification and classification of cross-working fault characteristics of the wind turbine- bearings are realized. The model achieves 99.48% transfer learning for the ten classifications of CWRU data set, 97% transfer learning for the four classifications of UPB data set, and 90% transfer learning for wind turbine bearing data across working conditions and across equipment. It is concluded that: Compared with similar models, the AMVMD-IDDTLN model proposed in this paper has higher diagnostic accuracy and faster convergence rate, which has certain practicality.

Nonlinear and nonstationary detection and quantification of multi-scale measured signals for bridge structure

The assessment of nonlinear and nonstationary levels in measured bridge signals is a vital step in system identification and long-term health monitoring for the bridge structure. The field-measured signals from the bridge structure are inherently weak and multiscale, so a specific adaptive variational mode decomposition (AVMD) is proposed to decompose them and extract their included multi-scale features. Combination the adaptability of empirical mode decomposition with the dimensionality reduction of principal component analysis, the number of inherent mode functions (IMFs) that need to be given in the conventional variational mode decomposition is adaptively determined in the proposed AVMD. The original measured signals from the bridge structure multiscale are subsequently decomposed by AVMD into the multiscale IMFs with the lowest cross-correlation. Then, the recurrence plot and recurrence quantification analysis are introduced into the detection and quantification of the measured signals, and the nonlinear and nonstationary quantification indexes are constructed to describe quantitatively the nonlinear and nonstationary levels. The stabilities and accuracies of three nonlinear and three nonstationary quantification indexes are comparatively discussed by the nonlinear and nonstationary detection and quantification of three well-defined simulated signals. The Shannon entropy and trapping time indexes are subsequently determined to quantify the nonlinear and nonstationary levels of the measured signals, respectively. Finally, the proposed algorithm and quantification indexes are applied to the nonlinear and nonstationary detection and quantification of the measured signals from the real-world bridge structures. It is shown from the validation and discussion that the proposed algorithm is available to detect and quantify the nonlinear and nonstationary levels of the measured multiscale signal from the real-world bridge structure.

RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN

To accurately evaluate the remaining life (RUL) of rolling bearings under small sample conditions and strong noise interference, a RUL prediction scheme using adaptive variational mode decomposition (VMD) and double-discriminator conditional CycleGAN (DD-cCycleGAN) is put forward. Combining chimp optimization algorithm (ChOA) with VMD, an adaptive VMD algorithm based on ChOA is presented, which selects effective mode components for reconstruction and reduces interference from strong background noise. A DD-cCycleGAN is developed to generate new samples which not only retain sample information of source domain, but also resemble samples of target one. A LSTM network after training is utilized to predict the bearing RUL in test samples. The performance of this scheme was validated by using the XJTU-SY bearing test dataset. The comparison analyses demonstrate this scheme has strong noise resistance and high accuracy.

Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Adaptive Parameter Selection Variational Mode Decomposition Based on Bayesian Optimization and Its Application to the Detection of ITSC in PMSM

Seismic Signal Analysis Based on Adaptive Variational Mode Decomposition for High-speed Rail Seismic Waves

Seismic Signal Analysis Based on Adaptive Variational Mode Decomposition for High-speed Rail Seismic Waves

Fault Location of Grid-connected Microgrid Lines Based on AVMD and Double-ended Traveling Wave Ranging

To enhance the accuracy of fault location in microgrid lines, a fault location method based on adaptive variational mode decomposition (AVMD) and double-ended traveling wave ranging is proposed. Firstly, a microgrid radial structure model, incorporating wind, light, and water systems, is established. The parameters of variational mode decomposition are optimized by using the Aquila optimizer (AO) algorithm, allowing the original fault signal to be decomposed into multiple modal components using AVMD. Only a limited number of modal components retain the fault signal information. The effective weighted peak relevance index (EWPR) is employed to select the sensitive modal components, identified as the four modes that best preserve the fault information, for signal reconstruction. Finally, the reconstructed signal is applied for fault localization. Simulation results indicate that the proposed method is scarcely influenced by the fault type, exhibiting superior accuracy compared to the single-ended traveling wave method.

Fault Feature Extraction Method for Rolling Bearings Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Variational Mode Decomposition

Due to the difficulty in dealing with non-stationary and nonlinear vibration signals using the single decomposition method, it is difficult to extract weak fault features from complex noise; therefore, this paper proposes a fault feature extraction method for rolling bearings based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods. CEEMDAN was used to decompose the signal, and the signal was then screened and reconstructed according to the component envelope kurtosis. Based on the kurtosis of the maximum envelope spectrum as the fitness function, the sparrow search algorithm (SSA) was used to perform adaptive parameter optimization for VMD, which decomposed the reconstructed signal into several IMF components. According to the kurtosis value of the envelope spectrum, the optimal component was selected for an envelope demodulation analysis to realize fault feature extraction for rolling bearings. Finally, by using open data sets and experimental data, the accuracy of envelope kurtosis and envelope spectrum kurtosis as a component selection index was verified, and the superiority of the proposed feature extraction method for rolling bearings was confirmed by comparing it with other methods.

Forecasting the hydropower unit vibration based on adaptive variational mode decomposition and neural network

The feature extraction, diagnosis and prediction of vibration and fault are important to maintain the stable operation of units with the large-scale popularization of virtual power plants in hydropower plants. Therefore, more accurate and suitable state detection methods and forecasting models for complex conditions are needed. VMD is limited by the presetting of mode number, penalty factor and initial center frequency, which greatly reduces the accuracy of the algorithm. In this study, IAVMD algorithm is proposed. The initial center frequencies and the mode number can be determined by the scale space plane and the energy loss coefficient. The numerical model of penalty factor is constructed based on center frequency differences. Finally, the mode numbers are combined and adjusted by autocorrelation and other methods. Through comparative analysis of simulation signal and experimental signal, IAVMD shows great improvements in complex signals. At the same time, it is proved that the forecasting model combining IAVMD with BO and LSTM has significantly improved the prediction accuracy. Through comparative analysis, the results prove that the prediction model constructed by this method has higher accuracy.

An efficient new adaptive variational mode decomposition algorithm for extracting adventitious lung sounds

The rapid and accurate extraction of adventitious lung sounds (ALSs) from original lung sounds is greatly significant for diagnosing lung diseases and assessing lung condition. Due to interference from heart sounds and various noises, the empirical mode decomposition (EMD), variational mode decomposition (VMD) and some other classical algorithms have poor performance. Here, an efficient new adaptive variational mode decomposition (AVMD) algorithm is proposed to improve the efficiency for extracting ALSs. For minimizing the bandwidth of each extracted mode and reducing frequency aliasing between modes, the proposed algorithm constructs a frequency-constrained model based on VMD. In order to further improve its performance, the algorithm adopts a strategy of gradually extracting each mode to determine the optimal number of modes for avoiding the under-decomposition or over-decomposition, and uses Pool Adjacent Violators (PAV) to automatically initialize the center frequency of each mode to reduce algorithm iteration steps. Based on the above improved characteristics, the experiments presented in this paper, which conducted on the artificial simulation signal and 102 groups of lung sounds from 20 patients with severe pulmonary diseases, highlight the proposed AVMD's superiority over the other five algorithms when comprehensively considering both decomposition quality and computational efficiency. Furthermore, the above results of 102 groups of lung sounds are statistically analyzed to obtain the characteristic frequency distribution of ALSs, which can provide important references for clinicians and demonstrate the potential application of the proposed algorithm in the diagnosis of pulmonary diseases.

Hybrid Energy Storage Power Allocation Strategy for Stabilizing Wind Power Fluctuations

In order to solve the problem of energy shortage, renewable energy such as wind power has developed rapidly worldwide. However, due to its time-varying and uncertain nature, wind power is prone to local fluctuations and operational safety hazards when connected to the power grid. In response to the stable grid connection problem of wind power generation, a hybrid energy storage system is proposed A method was proposed to achieve smooth power fluctuations in wind power generation using a model, and a charging and discharging control and allocation method for energy storage was proposed. Firstly, the preliminary power of hybrid energy storage was obtained using the model predictive control method, Then, an adaptive variational mode decomposition method was used to achieve energy distribution between batteries and supercapacitors. Finally, the measured data of a 100MW wind power plant in Xinjiang was analyzed as an example. The proposed method has been validated to not only achieve reasonable power allocation between hybrid energy storage systems, but also effectively reduce the impact of wind power generation on the power grid, achieving long-term safe operation of hybrid energy storage systems.

A parsimonious ensemble with optimal deep learning and secondary decomposition for short-term wind speed forecasting

Wind energy needs to be used efficiently, which depends heavily on the accuracy and reliability of wind speed forecasting. However, the volatility and nonlinearity of wind speed make this difficult. In volatility and nonlinearity reduction, we sequentially apply complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to secondarily decompose the wind speed data. This framework, however, requires effectively modeling multiple uncertainty components. Eliminating this limitation, we integrate crow search algorithm (CSA) with deep belief network (DBN) to generate a unified optimal deep learning system, which not only eliminates the influence of multiple uncertainties, but also only adopts DBN as a predictor to realize parsimonious ensemble. Two experiments demonstrate the superiority of this system.

An adaptive VNCMD and its application for fault diagnosis of industrial sewing machines

Due to the non-linearity and non-stationarity of sound signals from complex transmission structure and frequent start-stop of industrial equipment, such as sewing machine, its incipient faults cannot be accurately identified with classical fault diagnosis methods. Therefore, a new fault diagnosis method with an adaptive variational nonlinear chirp mode decomposition (AVNCMD) is proposed. Firstly, a pre-processing step of convex optimization with generalized minimax-concave penalty function and spline-kernelled chirplet transform is applied to obtain the time–frequency distribution (TFD) with energy concentration, and a peak frequency extension method is proposed to adaptively search initial instantaneous frequencies of AVNCMD on the TFD; then the Pearson correlation coefficient is introduced as the termination condition of the adaptive signal decomposition. Secondly, multi-domain features are extracted from the decomposed nonlinear chirp modes, and an optimal features set is constructed by the feature preference method of an improved ReliefF. Finally, the fault classification is realized by the extreme learning machine, whose input weights and biases are optimized via the salp swarm algorithm. The AVNCMD is testified to be accurate in simulation with nonlinear amplitude modulation–frequency modulation signals and be effective in actual sound signals of industrial sewing machines on four different states. Through the detailed analysis of the measured sound signals in factory, the proposed fault diagnosis method can identify four states of the industrial sewing machines with an overall identification accuracy of 95.0%.

Electric shock feature extraction method based on adaptive variational mode decomposition and singular value decomposition

AbstractThis paper proposes a feature extraction method combining adaptive variational mode decomposition (AVMD) and singular value decomposition (SVD) for electric shock fault‐type identification. The AVMD algorithm is utilized to adaptively decompose the electric shock signal into intrinsic mode components, each containing distinct frequency information. Subsequently, the correlation coefficient is employed to extract the intrinsic mode component with amplitudes greater than or equal to 0.1 ( ≥ 0.1). Feature extraction is then performed using SVD on the ≥ 0.1 intrinsic mode component, based on its maximum singular value and singular entropy. This approach effectively overcomes the limitation of the traditional VMD that necessitates manual K value setting. Moreover, it achieves dimensionality reduction and feature extraction of the intrinsic mode components through SVD, resulting in enhanced computational efficiency and fault identification accuracy. Extensive simulations demonstrate the remarkable recognition rates of electric shock fault types in animals and plants using the proposed AVMD‐SVD method, achieving a recognition rate as high as 99.25%. Comparative performance analysis further verifies the superiority of AVMD‐SVD over similar empirical mode decomposition‐SVD feature extraction techniques.

An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network

Random intermittency and high fluctuation limit the wind power prediction accuracy. Although current studies offer various solutions, the prediction accuracy and data fitting performance are not satisfactory during violently fluctuating periods. To address the issue, a novel hybrid model is proposed in this paper, which combines adaptive variational mode decomposition (VMD), temporal convolution network with convolutional block attention module (CBTCN), and gated recurrent unit (GRU). First, VMD is used for data decomposition. Due to the high fluctuation of wind power data, it is difficult to optimize parameters for VMD. Thus grey wolf crossover optimization algorithm (GSCSO) is proposed, which combines four optimization algorithms. Then, an important index, i.e., dynamic error entropy (DEE) is proposed as the fitness function to ensure decomposition integrity, mode complexity, and predictability of sub-sequences for the first time. Thereafter, by extracting deep temporal features with CBTCN, GRU is cascaded to further mine the temporal correlation of these features and predict the wind power. Multiple experiments are conducted, and the results demonstrate that the proposed hybrid model can track the peaks and troughs satisfactorily, especially during violently fluctuating periods. For instance, in three-step prediction, the RMSE reduces by over 40% compared with other advanced models.

Characteristic enhancement of RV reducer under non-stationary condition based on WDT-IF-SS-VMD

Because the working conditions of the rotate vector (RV) reducer are often reciprocating at variable speeds and the working environment is relatively complex, so the vibration signals collected have non-stationary vibration and complex environmental noise. A fault diagnosis method for RV reducer (WDT-IF-SS-VMD) is proposed, which combines instantaneous frequency (IF) trend graph based on current signal and the parameter adaptive variational mode decomposition (VMD) algorithm. Firstly, the current signal and vibration signal are collected synchronously, and the steady-state phase of vibration signal is intercepted according to the IF trend diagram obtained by wavelet decomposition transform (WDT) of the current signal. Secondly, the intercepted vibration signals are transformed into scale space, and the parameters of VMD are selected adaptively based on fuzzy C-means clustering. Then, the maximum kurtosis criterion is used to complete the extraction of sensitive components. Finally, the envelope analysis is carried out to complete the fault diagnosis. The measured signal analysis results show that this method can effectively separate and extract fault features of the RV reducer.