A Flexible DC Ranging Scheme Based on Variational Mode Decomposition and Teager Energy Operator

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

Vie des entreprises

Aiming at the shortcoming of harmonic detection in power systems, a method based on Variational Modal Decomposition (VMD) is presented in this paper. This method combines VMD with Fast Fourier Transform (FFT) so that VMD can adaptively find the modal decomposition number 'K'. K intrinsic mode functions (IMF) can be obtained by adaptive VMD decomposition of harmonic signals and Teager energy operator (TEO) analysis can be performed. By calculating the three sample points of the signal via TEO, the instantaneous amplitude and instantaneous frequency of one sample point can be acquired, so as to realize the harmonic detection of the power grid signal. Through the simulation of transient signal, the results of adaptive VMD are tested. At the same time, TEO and Hilbert transform (HT) are used to calculate the IMF respectively. Through contrastive analysis of the caculation, the feasibility and accuracy of the method mentioned in the article can be verified.

This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization objective function. The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. This improvement solves one of the major drawbacks of the conventional VMD method, that is, the need to manually set parameters. Then, an optimal IMF automatic selecting process is performed for single-failure faults and compound faults, according to the principles of the maximum weighted AFM indicator and maximum spectrum peak ratio (SPR), respectively. The sensitive IMFs are then subjected to an envelope demodulation analysis to obtain the fault characteristic frequency. The results of simulations and experiments show that the proposed method can effectively identify fault characteristics early, especially compound faults, demonstrating great potential for real-world applications.

Precise fault localization is vital for enhancing power grid reliability, reducing outage duration, and lowering maintenance expenses. This paper presents a fast and effective method that utilizes the application of voltage pulses with opposing polarities and identical durations to the transmission line. Analysis of traveling wave propagation under both normal and fault conditions indicates that the initial voltage wave observed in the non-injected phase stems from reflections at the fault point. The arrival time of the wavefront is accurately determined using variational mode decomposition (VMD) combined with the Teager energy operator (TEO) to pinpoint the fault location. Simulations conducted in PSCAD validate the efficacy of the proposed approach. This method demonstrates advantages including straightforward implementation, swift response, and superior accuracy compared to conventional techniques.

A new denoising algorithm and feature extraction algorithm that combine a new kind of permutation entropy (NPE) and variational mode decomposition (VMD) are put forward in this paper. VMD is a new self-adaptive signal processing algorithm, which is more robust to sampling and noise, and also can overcome the problem of mode mixing in empirical mode decomposition (EMD) and ensemble EMD (EEMD). Permutation entropy (PE), as a nonlinear dynamics parameter, is a powerful tool that can describe the complexity of a time series. NPE, a new version of PE, is interpreted as distance to white noise, which shows a reverse trend to PE and has better stability than PE. In this paper, three kinds of ship-radiated noise (SN) signal are decomposed by VMD algorithm, and a series of intrinsic mode functions (IMF) are obtained. The NPEs of all the IMFs are calculated, the noise IMFs are screened out according to the value of NPE, and the process of denoising can be realized by reconstructing the rest of IMFs. Then the reconstructed SN signal is decomposed by VMD algorithm again, and one IMF containing the most dominant information is chosen to represent the original SN signal. Finally, NPE of the chosen IMF is calculated as a new complexity feature, which constitutes the input of the support vector machine (SVM) for pattern recognition of SN. Compared with the existing denoising algorithms and feature extraction algorithms, the effectiveness of proposed algorithms is validated using the numerical simulation signal and the different kinds of SN signal.

The research about online monitoring and leakage automatic location of water distribution networks (WDN) has a wide range of applications that include water resource protection, monitoring, and allocation. Variational mode decomposition (VMD) and cross-correlation (CC) based leakage location is a popular and effective method in WDN. However, the value of K intrinsic mode functions (IMFs) based on VMD decomposition needs to be determined artificially, which affects the separation effect of signal frequency band characteristics directly. Hence, this work proposes an adaptive method to determine the parameter K of leakage vibration signal’s IMFs, which will be applied to automatic leakage location in WDN. Firstly, the number of saddle points in the frequency domain envelope of the sampled signal in different step sizes is calculated. The parameter K is determined according to the curvature change of the number of saddle points and the sampled signal. Finally, the selective IMFs are reconstituted into a new signal, which can determine a leak position using CC based time-delay estimation (TDE). To verify the effectiveness of the proposed algorithm, the different methods based on EMD and Fast ICA are compared. The experimental results demonstrate that the proposed parameter K value adaptive VMD (KVA-VMD) decomposition method is more suitable for leakage location in WDN.

An optimized VMD method and its applications in bearing fault diagnosis

Fault location based on variable mode decomposition and kurtosis calibration in distribution networks

Two-terminal traveling wave fault location based on successive variational mode decomposition and frequency-dependent propagation velocity

Due to the complicated structure, vibration signal of rotating machinery is multicomponent with nonstationary and nonlinear features, so it is difficult to diagnose faults effectively. Therefore, effective extraction of vibration signal characteristics is the key to diagnose the faults of rotating machinery. Mode mixing and illusive components existed in some conventional methods, such as EMD and EEMD, which leads to misdiagnosis in extracting signals. Given these reasons, a new fault diagnosis method, namely, variation mode decomposition (VMD), was proposed in this paper. VMD is a newly developed technique for adaptive signal decomposition, which can decompose a multicomponent signal into a series of quasi-orthogonal intrinsic mode functions (IMFs) simultaneously, corresponding to the components of signal clearly. To further research on VMD method, the advantages and characteristics of VMD are investigated via numerical simulations. VMD is then applied to detect oil whirl and oil whip for rotor systems fault diagnosis via practical vibration signal. The experimental results demonstrate the effectiveness of VMD method.

Background/Objectives: This paper introduced an image fusion algorithm based on Variational Mode Decomposition (VMD). Methods/Statistical Analysis: Image fusion is one of the image enhancement methods which results the image with better quality derived from a set of degraded images. Fused image contains more information than input images and it is efficient for visual perception and computer vision applications. This paper proposed an image fusion technique based on VMD for multi focus images. VMD has been a recently introduced non-recursive decomposition method, which decomposes the image into separate spectral bands called Intrinsic Mode Function (IMF) or modes. The modes are generated with respect to the associated central frequencies and they are band limited. Findings: A fusion rule based on weighing scheme is performed at the decomposition level for increasing the features by decreasing the mutual information. The reconstruction of the IMFs results the final fused image. The performance analysis of the proposed fusion method is experimented using standard objective quality metrics. The efficiency of the proposed method is determined by comparing the method with some state of the art methods. Application/Improvements: The image fusion using VMD is applicable to multi-resolution, multi model multi-sensor images.

In bearing fault diagnosis, identifying fault features under strong background noise is a challenging task. As the weak fault signal of bearing is often interfered by noise, it's easy to cause the signal-noise ratio to be low. We combine the advantages of both variational modal decomposition (VMD) and sparse Bayesian learning (SBL) algorithms and propose a Group-Sparsity Learning Algorithm for Bearing Fault Diagnosis (VMD-SBGSL) based on the VMD and SBL. Compared with the traditional SBL, the proposed framework captures the group-sparsity structure of fault pulses, not only its own accuracy, but also the influence of adjacent accuracy is considered. The method firstly performs variational modal decomposition on the fault signal, selects the optimal modal number K and selects the optimal Intrinsic Mode Function (IMF) according to specific rules. Then use Variational Bayesian Inference (VBI) to solve the optimized SBGSL algorithm to recover the faulty pulse signal. No prior knowledge and no manual tuning of parameters is required in this process. Finally, envelope analysis is performed on the extracted results to identify bearing fault features. The method is applied to the actual fault data of rolling bearings and the comparison with two existing methods was carried out. According to the results, the method can effectively eliminate the interference of noise and can achieve accurate diagnosis of rolling bearing faults.

This paper presents analysis and classification of a pathological speech called cold speech, which is recorded when the person is suffering from common cold. Nose and throat are affected by the common cold. As nose and throat play an important role in speech production, the speech characteristics are altered during this pathology. In this work, variational mode decomposition (VMD) is used for analysis and classification of cold speech. VMD decomposes the speech signal into a number of sub-signals or modes. These sub-signals may better exploit the pathological information for characterization of cold speech. Various statistics, mean, variance, kurtosis and skewness are extracted from each of the decomposed sub-signals. Along with those statistics, center frequency, energy, peak amplitude, spectral entropy, permutation entropy and Renyi's entropy are evaluated, and used as features. Mutual information (MI) is further employed to assign the weight values to the features. In terms of classification rates, the proposed feature outperforms the linear prediction coefficients (LPC), mel frequency cepstral coefficients (MFCC), Teager energy operator (TEO) based feature and ComParE feature sets (IS09-emotion and IS13-ComParE). The proposed feature shows an average recognition rate of 90.02 percent for IITG cold speech database and 66.84 percent for URTIC database.

Planetary gearboxes are widely used in the modern mechanical transmission equipment. The fault diagnosis of the planetary gearboxes is an important work. When the planetary gearbox fault occurs, its vibration signal has the characteristics of complex frequency components and amplitude modulation-frequency modulation. In view of these problems, a signal demodulation analysis method based on complementary ensemble empirical mode decomposition (CEEMD) and Teager energy operator is proposed to extract instantaneous amplitude and instantaneous frequency information of the fault signals. First, the CEEMD method is used to decompose the vibration signals of the complex multi component planetary gearbox into a series of single component intrinsic mode functions (IMFs). Then, the Teager energy operator is used to estimate the amplitude envelope and the instantaneous frequency of each single component signal. The intrinsic mode function is selected as the sensitive component according to the fluctuation characteristic of the instantaneous frequency. Finally, the Fourier transformation is applied in the obtained amplitude envelope and instantaneous frequency of the sensitive component to identify the gear fault characteristic frequencies, and achieve the accurate diagnosis of the planetary gearbox fault. The effectiveness and superiority of the method is verified through the analysis of the fault simulation experiment data of the planetary gearbox.