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

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.

The working environment of rotating machines is complex, and their key components are prone to failure. The early fault diagnosis of rolling bearings is of great significance; however, extracting the single scale fault feature of the early weak fault of rolling bearings is not enough to fully characterize the fault feature information of a weak signal. Therefore, aiming at the problem that the early fault feature information of rolling bearings in a complex environment is weak and the important parameters of Variational Modal Decomposition (VMD) depend on engineering experience, a fault feature extraction method based on the combination of Adaptive Variational Modal Decomposition (AVMD) and optimized Multiscale Fuzzy Entropy (MFE) is proposed in this study. Firstly, the correlation coefficient is used to calculate the correlation between the modal components decomposed by VMD and the original signal, and the threshold of the correlation coefficient is set to optimize the selection of the modal number . Secondly, taking Skewness (Ske) as the objective function, the parameters of MFE embedding dimension M, scale factor S and time delay T are optimized by the Particle Swarm Optimization (PSO) algorithm. Using optimized MFE to calculate the modal components obtained by AVMD, the MFE feature vector of each frequency band is obtained, and the MFE feature set is constructed. Finally, the simulation signals are used to verify the effectiveness of the Adaptive Variational Modal Decomposition, and the Drivetrain Dynamics Simulator (DDS) are used to complete the comparison test between the proposed method and the traditional method. The experimental results show that this method can effectively extract the fault features of rolling bearings in multiple frequency bands, characterize more weak fault information, and has higher fault diagnosis accuracy.

In order to extract fault features of rolling bearings to characterize their operation state effectively, an improved method, based on modified variational mode decomposition (MVMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), is proposed. Firstly, the MVMD method is introduced to decompose the vibration signal into intrinsic mode functions (IMFs), and then calculate the energy ratio of each IMF component. The IMF component is selected as the effective component from high energy ratio to low in turn until the total energy proportion Esum(t) ≥ 90%. The IMF effective components are reconstructed to obtain the subsequent analysis signal x_new(t). Secondly, the MOMEDA method is introduced to analyze x_new(t), extract the fault period impulse component x_cov(t), which is submerged by noise, and demodulate the signal x_cov(t) by Teager energy operator demodulation (TEO) to calculate Teager energy spectrum. Thirdly, matching the dominant frequency in the spectrum with the fault characteristic frequency of rolling bearings, the fault feature extraction of rolling bearings are completed. Finally, the experiments have compared MVMD-MOEDA-TEO with MVMD-TEO and MOMEDA-TEO based on two different data sets to verify the superiority of the proposed method. The experimental results show that MVMD-MOMEDA-TEO method has better performance than the other two methods, and provides a new solution for condition monitoring and fault diagnosis of rolling bearings.

In this paper, a new Cable fault location scheme is proposed for the existing flexible DC Cable fault location methods, in which the fault signal is mixed with noise and the traveling wave speed is affected by line parameters. This scheme is based on the propagation characteristics of fault line mode voltage traveling waves in flexible DC transmission. Firstly, the fault voltage is effectively decomposed using Variational Mode Decomposition (VMD). Then, the decomposed intrinsic mode functions (IMF) are reconstructed to obtain the high-frequency components of the recorded fault information. Finally, the traveling wave head is calibrated using the Teager Energy Operator (TEO). A dual terminal ranging algorithm that is not affected by wave velocity has been proposed, which can effectively avoid the impact of fault traveling wave velocity uncertainty on the ranging results. A dual ended flexible DC power grid model was built in PSCAD/EMTDC, and a large number of simulation results show that the algorithm proposed in this paper can accurately calibrate the fault traveling wave head, has little interference from transition resistance, strong noise resistance, and can obtain high-precision fault location results in different fault locations.

This paper proposes a new criterion for selecting the optimum number of Intrinsic Mode Functions (IMFs) from vibration signals measured on rotating machines. The criterion is based on calculating the variation of normalized Shannon entropy ([Formula: see text]) between two successive IMFs, enabling the optimization of the number of processed IMFs. The method combines Variational Mode Decomposition (VMD) with Wavelet Multi-Resolution Analysis (WMRA) using the newly introduced criterion. The key steps involve determining the optimal number of IMFs, decomposing the vibration signals using VMD, and applying WMRA to the selected IMFs. Numerical simulations and experimental results demonstrate its effectiveness in identifying various faults, including gear defects, shaft misalignment, insufficient backlash, and belt defects. These latter flaws are the primary cause of the elevated noise levels in the measured signals. Finally, to validate our approach in an industrial setting, we diagnosed a turbofan, which revealed the presence of several typical faults for this type of installation. The proposed approach addresses a critical industry need by increasing fault diagnosis accuracy in noisy environments. Its practical impact stems from its ability to improve early fault detection, which is critical for predictive maintenance strategies aimed at reducing equipment downtime and extending its lifespan.

The incipient fault feature of rolling bearing is difficult to extract from vibration signals for the weak energy and nonstationarity. A method of extracting incipient bearing fault features was proposed in this paper, which based on the optimized singular spectrum decomposition (OSSD) with 1.5-dimensional symmetric differential energy operator (1.5D-SDEO) demodulation. First, the slime mould algorithm (SMA) is used to optimize the input parameter of the singular spectrum decomposition (SSD). Second, the bearing vibrational signal is decomposed into different singular spectrum components (SSC) by the proposed OSSD, and the SSC selected with the largest syncretic impact index (SII). Finally, the selected SSC is demodulated with 1.5D-SDEO, which uses symmetric differential energy operator (SDEO) instead of teager energy operator (TEO) in 1.5-dimensional TEO (1.5D-TEO). Simulation and experimental analysis were conducted to verify the validity of the proposed method. Results demonstrate that the proposed method could provide a new thought on the incipient fault feature extraction of rolling bearings.

An operating condition information-guided iterative variational mode decomposition method based on Mahalanobis distance criterion for surge characteristic frequency extraction of the centrifugal compressor

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery.

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types.

The original signal of rolling bearing fault contains a large number of phase coupling components and is easily submerged in the background noise, which make the fault information difficult to be extracted accurately. Aiming at the above problems, a method of fault feature extraction for rolling bearing is proposed, which combines empirical mode decomposition (EMD) with a 1.5-dimensional spectrum. The original signal is decomposed by EMD to obtain the intrinsic modal function (IMF) of different scales. The IMF is selected by the size of a correlation coefficient and a kurtosis value to eliminate the high-frequency components, which is reconstructed to achieve the purpose of noise reduction. The reconstructed Hilbert envelope signal is analysed by the 1.5-dimensional spectrum to extract the nonlinear characteristic of two phase couplings, so that the fault characteristic frequency of the bearing is obtained. By analysing the signal of actual rolling bearings, the fault characteristic frequency of bearing inner and outer rings can be effectively extracted, and the validity and feasibility of the method are proved.

Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising

The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

The bearing fault diagnosis is of vital significance in maintaining the safety of rotation machine. Among various fault detection techniques, the diagnosis based on vibration signal is widely applied in monitoring the condition of rotation machine. Variational mode decomposition (VMD) is a novel signal analysis method, which can decompose a multi-component signal into a certain number of band-limited intrinsic mode functions (BLIMFs) nonrecursively. VMD could overcome some problems such as mode mixing, the inference of noise, the determination of wavelet base, which exist in empirical mode decomposition, ensemble empirical mode decomposition, wavelet transform, respectively. However, the empirical selection of the parameters for VMD would affect the result of the decomposition. This paper presents an adaptive VMD method with parameter optimization for detecting the localized faults of rolling bearing. Kurtosis, sensitive to transient impulsive components, is employed as optimization index to evaluate the performance of the VMD. Two parameters in the VMD, namely the number of decomposition modes and data-fidelity constraint, are optimized synchronously based on the kurtosis index through artificial fish swarm algorithm. Executing VMD with the acquired parameters, the optimal BLIMF is obtained. The spectrum analysis of the optimal BLIMF could identify the characteristic frequency caused by the localized crack effectually. The validity of the proposed method is proved by means of a cyclic transient impulse response signal and two experiments with practical vibration signals of rolling bearings. Compared to several existing methods, the proposed method demonstrates reinforced results.

An approach of fault diagnosis of bearing based on empirical mode decomposition (EMD), sample entropy and 1.5 dimension spectrum was presented. Firstly, the original vibration signal was decomposed into a number of intrinsic mode functions (IMFs) using EMD. Second, the sample entropies of IMFs were calculated to select the sensitive IMF. Finally, the IMF containing fault infor- mation was analyzed with 1.5 dimension spectrum, The experimental results show the method can be used to effectively diagnose faults of rolling bearing.