A novel approach of fault diagnosis for gearbox based on VMD optimized by GSWOA and improved RCMSE

Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

In order to improve the fault diagnosis accuracy of bearings, an intelligent fault diagnosis method based on Variational Mode Decomposition (VMD), Composite Multi-scale Dispersion Entropy (CMDE), and Deep Belief Network (DBN) with Particle Swarm Optimization (PSO) algorithm—namely VMD-CMDE-PSO-DBN—is proposed in this paper. The number of modal components decomposed by VMD is determined by the observation center frequency, reconstructed according to the kurtosis, and the composite multi-scale dispersion entropy of the reconstructed signal is calculated to form the training samples and test samples of pattern recognition. Considering that the artificial setting of DBN node parameters cannot achieve the best recognition rate, PSO is used to optimize the parameters of DBN model, and the optimized DBN model is used to identify faults. Through experimental comparison and analysis, we propose that the VMD-CMDE-PSO-DBN method has certain application value in intelligent fault diagnosis.

In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level K based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of K in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don't rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA). Firstly, VMD was selected from various signal decomposition methods to decompose the original signal. Then, the signal features were extracted by RCMDE as the input of the diagnosis model. Compared with multiscale sample entropy (MSE) and multiscale dispersion entropy (MDE), RCMDE proved to be superior. Afterwards, SSA was used to search the optimal parameters of SVM to identify different faults. Finally, the proposed coordinated VMD–RCMDE–SSA–SVM approach was verified and evaluated by the experimental data collected by the wind turbine drivetrain diagnostics simulator (WTDS). The results of the experiments demonstrate that the proposed approach not only identifies bearing fault types quickly and effectively but also achieves better performance than other comparative methods.

To obtain the fault features of the bearing, a method based on variational mode decomposition (VMD), singular value decomposition (SVD) is proposed for fault diagnosis by Gath–Geva (G–G) fuzzy clustering. Firstly, the original signals are decomposed into mode components by VMD accurately and adaptively, and the spatial condition matrix (SCM) can be obtained. The SCM utilized as the reconstruction matrix of SVD can inherit the time delay parameter and embedded dimension automatically, and then the first three singular values from the SCM are used as fault eigenvalues to decrease the feature dimension and improve the computational efficiency. G–G clustering, one of the unsupervised machine learning fuzzy clustering techniques, is employed to obtain the clustering centers and membership matrices under various bearing faults. Finally, Hamming approach degree between the test samples and the known cluster centers is calculated to realize the bearing fault identification. By comparing with EEMD and EMD based on a recursive decomposition algorithm, VMD adopts a novel completely nonrecursive method to avoid mode mixing and end effects. Furthermore, the IMF components calculated from VMD include large amounts of fault information. G–G clustering is not limited by the shapes, sizes and densities in comparison with other clustering methods. VMD and G–G clustering are more suitable for fault diagnosis of the bearing system, and the results of experiment and engineering analysis show that the proposed method can diagnose bearing faults accurately and effectively.

To address the challenge of anomaly detection in vibration signals from high-speed electric multiple unit (EMU) motor bearings, characterized by strong non-stationarity and multi-component coupling, this study proposes a synergistic approach integrating variational mode decomposition (VMD) and deep learning. Unlike datasets focused on fault diagnosis (identifying known fault types), anomaly detection identifies deviations into unknown states. The method utilizes real-world, non-real-time vibration data from ground monitoring systems to detect anomalies from early signs to significant deviations. Firstly, adaptive VMD parameter selection, guided by power spectral density (PSD), optimizes the number of modes and penalty factors to overcome mode mixing and bandwidth constraints. Secondly, a hybrid deep learning model integrates convolutional neural networks (CNNs), bidirectional long- and short-term memory (BiLSTM), and residual network (ResNet), enabling precise modal component prediction and signal reconstruction through multi-scale feature extraction and temporal modeling. Finally, the root mean square (RMS) features of prediction errors from normal operational data train a one-class support vector machine (OC-SVM), establishing a normal-state decision boundary for anomaly identification. Validation using CR400AF EMU motor bearing data demonstrates exceptional performance: under normal conditions, root mean square error , Mean Absolute Error , and Coefficient of Determination ; for anomaly detection, accuracy = 95.2% and F1-score = 0.909, significantly outperforming traditional methods like Isolation Forest (F1-score = 0.389). This provides a reliable technical solution for intelligent operation and maintenance of EMU motor bearings in complex conditions.

Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

Mechanical fault diagnosis of a circuit breaker can help improve the reliability of power systems. Therefore, a new method based on multiscale entropy (MSE) and the support vector machine (SVM) is proposed to diagnose the fault in high voltage circuit breakers. First, Variational Mode Decomposition (VMD) is used to process the high voltage circuit breaker’s vibration signals, and the reconstructed signal can eliminate the effect of noise. Second, the multiscale entropy of the reconstructed signal is calculated and selected as a feature vector. Finally, based on the feature vector, the fault identification and classification are realized by SVM. The feature vector constructed by multiscale entropy is compared with other feature vectors to illustrate the superiority of the proposed method. Through experimentation on a 35 kV SF6 circuit breaker, the feasibility and applicability of the proposed method for fault diagnosis are verified.

Health condition identification of planetary gearboxes based on variational mode decomposition and generalized composite multi-scale symbolic dynamic entropy

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.

Variational mode decomposition is a new signal decomposition method, which can process non-linear and non-stationary signals. It can overcome the problems of mode mixing and compensate for the shortcomings in empirical mode decomposition. Permutation entropy is a method which can detect the randomness and kinetic mutation behavior of a time series. It can be considered for use in fault diagnosis. The complexity of wind power generation systems means that the randomness and kinetic mutation behavior of their vibration signals are displayed at different scales. Multi-scale permutation entropy analysis is therefore needed for such vibration signals. This research investigated a method based on variational mode decomposition and permutation entropy for the fault diagnosis of a wind turbine roller bearing. Variational mode decomposition was adopted to decompose the bearing vibration signal into its constituent components. The components containing key fault information were selected for the extraction of their permutation entropy. This entropy was used as a bearing fault characteristic value. The nearest neighbor algorithm was employed as a classifier to identify faults in a roller bearing. The experimental data showed that the proposed method can be applied to wind turbine roller bearing fault diagnosis.

In response to the problems of low accuracy and poor noise immunity of the traditional fault diagnosis method for rolling bearing fault diagnosis due to the complex and variable operating conditions of rolling bearings and the large noise interference during bearing signal acquisition, a rolling bearing fault diagnosis model based on VMD–RP–CSRN is proposed. Firstly, the initial feature extraction of the bearing signal is carried out by variational modal decomposition (VMD), which is then converted into a two-dimensional image with fault features by recurrent plot (RP) coding, and then the feature images are input to a channel split residual network (CSRN) for feature extraction and fault classification. In order to verify the accuracy and noise immunity of the proposed method for the diagnosis of bearing faults under complex working conditions, experiments on the selection of parameters in the CSRN model were conducted on the bearing dataset of Jiangnan University, and experiments on the diagnosis of bearing faults under complex working conditions and noise immunity of CSRN were carried out and compared with other commonly used methods. The proposed bearing fault diagnosis method based on VMD–RP–CSRN combines VMD and RP to retain the fault features in the original signal to the maximum extent and stress the hidden features in the signal. The proposed channel split operation realizes the extraction of hidden features by selecting the main operating channel of the three-channel feature image, and makes more fault features participate in the feature extraction of the diagnosis model. The experimental results demonstrate that the proposed method is at least 1.2% better than the comparison method, and has better noise immunity. In addition, experiments on the fault diagnosis capability of the model with different data set sizes and the diagnosis of variable speed bearing data by the model show that the proposed method has better generalization performance and diagnosis capability.

This paper presents a fault diagnosis method based on wavelet threshold (WT), variational mode decomposition (VMD) and random forest. First, wavelet threshold method is used to preprocess the original vibration signal to reduce noise interference and enhance fault features; then VMD is used to decompose the denoised signal into several intrinsic mode functions (IMFs) and extract the center frequencies of each IMF; finally, these center frequencies will be input into the random forest classifier as feature vectors for fault type identification. Compared with the results of some existing fault diagnosis methods (such as EMD + variable prediction model, VMD + PSO-PNN, VMD + multi-scale permutation entropy + SVM), it is found that this method greatly improves the accuracy of fault identification. The research results show that this method can effectively identify various fault types of rolling bearing.

Fault detection based on external leakage flux can address various faults, such as short circuit faults in stators and rotors. External leakage flux sensing technology, as a noninvasive method, has been attracting increasing attention and research. However, for a high-power doubly fed induction generator (DFIG), the external leakage flux signals of the generator are easily overwhelmed by a strong noise background, and the fault diagnosis and recognition of stator and rotor interturn short circuits is complicated, which limits the practical engineering application of leakage flux sensing technology. Aiming at addressing interturn short circuit faults in DFIGs, this paper proposes a method for fault feature extraction and recognition of stator and rotor short circuits based on variational mode decomposition (VMD) and the refined composite multiscale dispersion entropy (RCMDE) analytical method for the external flux leakage of the generator. The optimized parameters of VMD are selected automatically by the genetic algorithm (GA), and VMD is adopted to adaptively decompose the external leakage flux signals into a series of intrinsic mode function (IMF) components. The evaluation criterion based on the correlation number in the frequency domain combined with the autocorrelation function is used to select the best IMF components with clear features. Two different components are chosen to reconstruct the flux leakage signal. The characteristic frequency of the reconstructed signal is analyzed by the Hilbert-Huang transform (HHT) and the total harmonic effective value of the characteristic component. To effectively identify and diagnose the generator stator and rotor interturn short circuit fault, RCMDE is used for the flux leakage reconstructed signal. The experimental results under different stator and rotor short-circuit levels show that this diagnosis method can effectively extract the weak feature information from the external flux leakage signals and perform fault feature extraction and recognition for stator and rotor short circuits.

To address the challenges of insufficient feature extraction and poor robustness in rolling bearing fault diagnosis, this paper proposes a novel method termed Hybrid-Domain Features and DBO-Optimized SVM (Hybrid Features-DBO-SVM). Vibration signals are first preprocessed using Variational Mode Decomposition (VMD), with its key parameters optimized by the Osprey–Cauchy-enhanced Sparrow Search Algorithm (OCSSA) for effective denoising. Subsequently, Refined Composite Multiscale Permutation Entropy (RCMPE) is extracted to quantify signal complexity. Statistical time-domain features (e.g., kurtosis and peak value) are then fused with RCMPE to construct a discriminative hybrid-domain feature vector characterizing diverse bearing conditions. To mitigate the parameter sensitivity of the Support Vector Machine (SVM), the Dung Beetle Optimization (DBO) algorithm is employed to adaptively optimize its hyperparameters. Validation on the Jiangnan University bearing dataset demonstrates a diagnostic accuracy of 97.2%, surpassing comparative models including K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and alternative feature extraction methods in both computational efficiency and classification precision. Further validation on the Case Western Reserve University (CWRU) dataset confirms the method’s robustness, advancement, and capability for rapid, accurate fault identification, providing a novel and robust solution for bearing condition monitoring and fault diagnosis.