Rolling Bearing Fault Diagnosis Method Research Articles

Rolling bearing fault diagnosis method based on 2D grayscale images and Wasserstein Generative Adversarial Nets under unbalanced sample condition

Accurate diagnosis of rolling bearing faults plays a crucial role in ensuring the stable operation of rotating machinery systems. However, in actual engineering applications, a significant disparity between the volume of normal data and the quantity of fault data collected impairs diagnostic performance. Bearing fault diagnosis under sample imbalance conditions is an engineering challenge encountered in the field of fault diagnosis. To improve the fault diagnosis accuracy under unbalanced sample conditions, a rolling bearing fault diagnosis method based on 2D grayscale images and Wasserstein Generative Adversarial Networks (WGAN) is proposed. The method consists of three main steps. First, the acquired bearing vibration signals are transformed into 2D grayscale images. Second, the WGAN generation model is used to generate more fault samples. Finally, both the original samples and the generated samples are used to train the Convolutional Neural Networks classification model. The validity and effectiveness of the proposed method are evaluated and compared to other bearing fault diagnosis approaches using the Case Western Reserve University Bearing Data Center dataset. The experimental results demonstrate the superior quality of the generated samples and the improved fault identification accuracy achieved by the proposed method.

Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer

Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with the Vision Transformer. Firstly, the one-dimensional vibration signal is preprocessed to reduce noise using singular value decomposition (SVD) to obtain a more accurate and useful signal. Then, the generalized S-transform (GST) is used to convert the processed one-dimensional vibration signal into a two-dimensional time–frequency image and make full use of the advantages of deep learning in image classification with higher recognition accuracy. In order to avoid the problem of limited sensory fields in CNN and the need for an RNN to compute step by step over time when processing sequence data, the use of a Vision Transformer model for pattern recognition classification is proposed. Finally, an experimental platform for the fault diagnosis of rolling bearings is built. The model is experimentally validated, achieving an average accuracy of 98.52% over multiple tests. Additionally, compared with the SVD-GST-2DCNN, STFT-CNN-LSTM, SVD-GST-LSTM, and GST-ViT fault diagnosis models, the proposed method has higher diagnostic accuracy and stability, providing a new method for rolling bearing fault diagnosis.

Instantaneous Square Current Signal Analysis for Motors Using Vision Transformer for the Fault Diagnosis of Rolling Bearings

Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received widespread attention in recent years. Meanwhile, traditional fault diagnosis methods cannot meet the diagnostic accuracy requirements because of the low signal-to-noise ratio (SNR) of the MCS. Committed to achieving bearing fault diagnosis through MCS, a rolling bearing fault diagnosis method, ISCV-ViT, based on the MCS and the Vision Transformer (ViT) model, is proposed. In particular, a signal processing method based on the instantaneous square current value (ISCV) is proposed to process the MCS directly obtained through a frequency converter into time-domain images. Then, the ViT model is applied for bearing fault diagnosis. Finally, experimental verification is carried out based on the public bearing dataset of Paderborn University (PU) and the bearing dataset of Shenzhen Technology University (SZTU). The analysis of the experimental results demonstrates that the average accuracy of the ISCV-ViT for the two datasets is up to 96.60% and 94.87%, respectively.

Research on Rolling Bearing Fault Diagnosis Based on Volterra Kernel Identification and KPCA

A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the bearing in four states: normal, rolling element fault, inner ring fault, and outer ring fault. The Volterra time-domain kernel is used as the feature vector for kernel principal component analysis to classify and identify the faults. The feasibility of the fault diagnosis method of the Volterra level and kernel principal component analysis is verified by the experimental results.

Rolling bearing fault diagnosis based on multi-scale weighted visibility graph and multi-channel graph convolution network

Current data-driven fault diagnosis methods are prone to overfitting and a decrease in accuracy when working with only a limited number of labeled samples. Additionally, existing graph neural network-based fault diagnosis methods often fail to comprehensively utilize both global and local features. To address these challenges, we propose a rolling bearing fault diagnosis method based on multi-scale weighted visibility graph and a multi-channel graph convolutional network (MCGCN). Our approach converts vibration signals into multiple weighted graphs from the perspective of geometric meaning and extracts local node feature information and global topology information of graphs using MCGCN. Experimental results demonstrate that our method achieves excellent performance under both sufficient and limited data conditions, providing a promising approach for real-world industrial bearing fault diagnosis.

Rolling bearing fault diagnosis method based on ELMD hybrid feature extraction and wavelet neural network

Rolling bearings are the most important components in the transmission system of coal mining machinery, and their operating condition significantly impacts the entire mechanical and electrical equipment. Therefore, the fault diagnosis of rolling bearing can effectively ensure the operation reliability of equipment. Given the strong noise, coal impact, and other interference, the vibration signal of the rolling bearing cannot be effectively decomposed, and the fault identification efficiency is low. According to the method based on vibration analysis, this article proposes a rolling bearing fault diagnosis method based on ensemble local mean decomposition (ELMD) hybrid feature extraction and wavelet neural network. ELMD is used to solve the problem of modal aliasing in local mean decomposition (LMD), which can improve the efficiency of LMD. Quantitatively extracting the mixed features of each component and introducing a wavelet neural network for fault type recognition. The experimental results demonstrate that the proposed method has a high accuracy in fault recognition and is an effective fault diagnosis method.

A novel rolling bearing fault diagnosis method based on continuous hierarchical fractional range entropy

Recognizing rolling bearing faults heavily relies on effective feature extraction methods. In practical engineering, the collected vibration data contains significant levels of noise and amplitude fluctuations, impeding the successful extraction of fault features. To address these challenges, a fault extraction method known as continuous hierarchical fractional range entropy (CHFRE) is proposed. First, range entropy (RE) is introduced and derived to the fractional domain to quantify the fault characteristics. This can overcome the effects of high noise and amplitude fluctuations. Second, the fault features hidden in the low- and high-frequency components are extracted in the multiscale domain by improved hierarchical analysis. Finally, a model for rolling bearing faults identification is established. This paper explores the CHFRE parameters, and a crucial finding is presented: the fractional order contains several invalid intervals within its defined range. The validity of the method and the existence of the invalid intervals are verified by two experiments.

A novel rolling bearing fault diagnosis method based on parameter optimization variational mode decomposition with feature weighted reconstruction and multi-target attention convolutional neural networks under small samples.

To enhance the precision of rolling bearing fault diagnosis, an intelligent hybrid approach is proposed in this paper for signal processing and fault diagnosis in small samples. This approach is based on advanced techniques, combining parameter optimization variational mode decomposition weighted by multiscale permutation entropy (MPE) with maximal information coefficient and multi-target attention convolutional neural networks (MTACNN). First, an improved variational mode decomposition (VMD) is developed to denoise the raw signal. The whale optimization algorithm was used to optimize the penalty factor and mode component number in the VMD algorithm to obtain several intrinsic mode functions (IMFs). Second, separate MPE calculations are performed for both the raw signal and each of the IMF components obtained from the VMD decomposition; the results are used to calculate the maximum information coefficient (MIC). Subsequently, each MIC is normalized and converted to a weight coefficient for signal reconstruction. Ultimately, the reconstructed signals serve as input to the MTACNN for diagnosing rolling bearing faults. Results demonstrate that the signal processing approach exhibits superior noise reduction capability through simple processing. Furthermore, compared to several similar approaches, The method proposed for fault diagnosis achieves superior performance levels in the fault pattern recognition target and the fault severity recognition target.

Research on Fault Diagnosis Method of Rolling Bearing Based on Variational Mode Decomposition and Gath-Geva Clustering

For the hassle that the fault of the rolling bearing is challenging to identify, a method for identifying the damage degree of rolling bearing based on the combination of Variational Mode Decomposition (VMD) and Gath-Geva (GG) fuzzy clustering is proposed. The method decomposes the known rolling bearing fault signal by VMD, determines the number of decomposed modes based on the magnitude of the component frequency center, and forms the obtained eigenmode components into an initial eigenmatrix for singular value decomposition. The three largest singular values are selected as the input of the GG clustering algorithm, and the membership matrix and cluster center of the known fault signals are obtained. The damage degree of the rolling bearing is identified by the Hamming closeness between the initial membership matrix of the signal to be measured and the cluster centers with clear fault signal. Rolling bearings vibration data verify the effectiveness of the method. The experimental results show that the method in this paper can accurately extract the fault information contained in the vibration signal, realize the correct fault type identification, and provide a reliable basis for the rolling bearing fault diagnosis method.

A Rolling Bearing Fault Diagnosis Method Based on Improved CEEMDAN and RCMFE

Considering the problem of residual noise and spurious modes in the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a rolling element bearing malfunction diagnostic method based on improved CEEMDAN (ICEEMDAN) is proposed. First, different from the CEEMDAN, which directly adds Gaussian white noise with a mean of zero, the proposed method adds the [Formula: see text]th component obtained from white noise decomposed by empirical mode decomposition (EMD) to the vibration signal, and then the ICEEMDAN is employed to decompose the signal into several intrinsic mode functions (IMFs). Second, aiming at the uncertainty problem of entropy estimation in multi-scale fuzzy entropy (MFE), a refined composite multi-scale fuzzy entropy (RCMFE) is proposed to obtain the characteristic from the selected IMFs. Finally, smoothing factor of PNN is determined by fruit fly optimization algorithm (FOA), and the extracted features are input into the FOA-PNN model to achieve condition identification. Experimental results illustrate that the identification accuracy is more than 99%, which indicates its high effectiveness and superiority.

Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function

In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration signals are made into input samples to retain the original features to the maximum extent. Secondly, the CBAM_ResNet fault diagnosis model is constructed. By taking advantage of the convolutional neural network (CNN) in classification tasks and key feature extraction, the convolutional block attention module network (CBAM) is embedded in the residual blocks, to avoid model degradation and enhance the interaction of information in channel and spatial, raise the key feature extraction capability of the model. Finally, the Activate or Not (ACON) activation function, is introduced to adaptively activate shallow features for the purpose of improving the model’s feature representation and generalization capability. The bearing dataset of Case Western Reserve University (CWRU) is used for experiments, and the average accuracy of the proposed method is 97.68% and 93.93% under strong noise interference and variable load, respectively. Compared with the other three published bearing fault diagnosis methods, the results indicate that this proposed method has better noise immunity and generalization ability, and has good application value.

A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM

Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling bearing fault diagnosis method that addresses the above-mentioned problem through the moth-flame optimization algorithm optimized variational mode decomposition (MFO-optimized VMD) and an ensemble differential evolution online sequential extreme learning machine (DE-OSELM). By using the dynamic adaptive weight factor and genetic algorithm cross operator, the optimization accuracy and global optimization ability of the moth-flame optimization (MFO) are improved, and the two basic parameters of VMD decomposition level and quadratic penalty factor are adaptive selected. Since the vibration characteristics of the signal cannot be fully interpreted by a single index, The effective weighted correlation sparsity index (EWCS) is utilized to extract the relevant intrinsic mode functions (IMF) of VMD decomposition and extract their energies as features. In order to improve the classification accuracy, The energy feature set is subsequently inputted into DE-OSELM for training and classification purposes, and the proposed method is assessed via a sample set with four different health states of actual rolling bearings. Our proposed method results are compared with other diagnosis methods, proving its feasibility to diagnose rolling bearing faults with higher classification accuracy.

An improved GNN using dynamic graph embedding mechanism: A novel end-to-end framework for rolling bearing fault diagnosis under variable working conditions

Traditional deep learning (DL)-based rolling bearing fault diagnosis methods usually use signals collected under specific working condition to train the diagnosis models. This may lead to the lack of domain adaptive ability of these trained models, thus making it difficult to obtain satisfactory diagnosis accuracy when working conditions fluctuate. To address it, a novel fault diagnosis framework based on the graph neural network (GNN) and dynamic graph embedding mechanism (DGE) was proposed in this paper. Firstly, convolutional neural network (CNN) is used to extract the hidden fault features from raw bearing vibration signals. Secondly, DGE module is designed with edge dropout mechanism to transform the features exacted by CNN into higher-level graph-structured features dynamically. Then, GNN is applied to further mine the fault features sensitivity to the fluctuating bearing working conditions. Finally, a novel mechanism named node voters is proposed to replace traditional graph-level attribute update function in GNN to obtain optimal fault pattern recognition results. Experiment results shows that the proposed framework can not only realize the end-to-end fault diagnosis of rolling bearings, but also has excellent domain adaptive ability to obtain better stability and diagnosis accuracy under variable working conditions compared to traditional DL-based methods.

Multi-sensor information fusion and coordinate attention-based fault diagnosis method and its interpretability research

It is always challenging and meaningful to further enhance the feature extraction capability of the convolutional neural network (CNN) and understand the internal working principle of CNN. In order to ensure that CNN focuses on more key information in the one-dimensional signal and reduces the attention to other information, a novel CNN network architecture based on the fusion of multi-sensor information and coordinated attention (CA) is proposed and applied to fault diagnosis of rolling bearings. Further, we studied the interpretability of the proposed network and intuitively revealed the feature learning process and final expression form of the proposed network. First, a coordinated attention (CA) module suitable for one-dimensional vibration signals is proposed, and a lightweight coordinated attention convolution neural network model ACNN is established. Secondly, a kurtosis-weighted fusion strategy is designed to enhance the shock feature of the sensor channels. Then, based on ACNN and weighting fusion strategy, a fusion framework for multi-sensor information (Multi-sensor ACNN) is constructed. Finally, based on the Multi-sensor ACNN, a new rolling bearing fault diagnosis method is proposed.

Diagnosisformer: An efficient rolling bearing fault diagnosis method based on improved Transformer

Aiming at the problems of low accuracy and robustness of traditional deep learning fault diagnosis methods, a novel attention-based multi-feature parallel fusion model Diagnosisformer is proposed for rolling bearing fault diagnosis utilizing Transformer as the basic network. Firstly, frequency domain features of the original data are extracted by Fast Fourier Transform (FFT), and then normalization operations and embeddings are performed on the model input. Secondly, the designed multi-feature parallel fusion encoder is exploited to extract the local and global features of the bearing data. The extracted features are fed to a cross-flipped decoder, followed by a classification head for fault classification. Finally, experimental verification is performed using data collected by the rotating machinery fault diagnosis experimental platform and the Case Western Reserve University (CWRU) bearing dataset. The average experimental results on the two fault diagnosis datasets are 99.84% and 99.85%, respectively. The results show that our diagnosis method significantly outperforms the state-of-the-art in accuracy, generalization, and robustness.

A novel rolling bearing fault diagnosis method based on Adaptive Denoising Convolutional Neural Network under noise background

In recent years, significant progress has been made in intelligent fault diagnosis algorithms for rolling bearings. However, their real industrial application performance is hindered by challenges related to noise and variable load conditions. To solve this problem, we proposed an adaptive denoising convolutional neural network (ADCNN) which integrates adaptive denoising units to remove noise while preserving sensitive fault features, eliminating the need for manual denoising function settings. In addition, we use Maximum Overlap Discrete Wavelet Packet Transform to separate out the interfering components of noisy signal. To further improve ADCNN's noise immunity, we adopt a strategy of gradually decreasing the number of channels and using large convolutional kernels. ADCNN was evaluated alongside the latest methods on two different datasets, and the results demonstrate that ADCNN outperforms other methods both accuracy and robustness. Therefore, our approach presents a promising solution for diagnosing mechanical systems in noisy environments.

Rolling Bearing Fault Diagnosis Based On Convolutional Capsule Network

Fault diagnosis technology has been widely applied and is an important part of ensuring the safe operation of mechanical equipment. In response to the problem of frequent faults in rolling bearings, this paper designs a rolling bearing fault diagnosis method based on Convolutional Capsule Network (CCN). More specifically, the original vibration signal is converted into a two-dimensional time-frequency image using continuous wavelet transform (CWT), and the feature extraction is performed on the two-dimensional time-frequency image using the convolution layer at the front end of the network, and the extracted features are input into the capsule network. The capsule network converts the extracted features into vector neurons, and the dynamic routing algorithm is used to achieve feature transfer and output the results of fault diagnosis. Two different datasets are used to compare with other traditional deep learning models to verify the fault diagnosis capability of the method. The results show that the convolutional capsule network has good diagnostic capability under different working conditions, even in the presence of noise and insufficient samples, compared to other models. This method contributes to the safe and reliable operation of mechanical equipment and is suitable for other rotating scenarios.

A Rolling Bearing Fault Diagnosis Method Based on the WOA-VMD and the GAT.

In complex industrial environments, the vibration signal of the rolling bearing is covered by noise, which makes fault diagnosis inaccurate. In order to overcome the effect of noise on the signal, a rolling bearing fault diagnosis method based on the WOA-VMD (Whale Optimization Algorithm-Variational Mode Decomposition) and the GAT (Graph Attention network) is proposed to deal with end effect and mode mixing issues in signal decomposition. Firstly, the WOA is used to adaptively determine the penalty factor and decomposition layers in the VMD algorithm. Meanwhile, the optimal combination is determined and input into the VMD, which is used to decompose the original signal. Then, the Pearson correlation coefficient method is used to select IMF (Intrinsic Mode Function) components that have a high correlation with the original signal, and selected IMF components are reconstructed to remove the noise in the original signal. Finally, the KNN (K-Nearest Neighbor) method is used to construct the graph structure data. The multi-headed attention mechanism is used to construct the fault diagnosis model of the GAT rolling bearing in order to classify the signal. The results show an obvious noise reduction effect in the high-frequency part of the signal after the application of the proposed method, where a large amount of noise was removed. In the diagnosis of rolling bearing faults, the accuracy of the test set diagnosis in this study was 100%, which is higher than that of the four other compared methods, and the diagnosis accuracy rate of various faults reached 100%.

Research on rolling bearing fault diagnosis method based on AMVMD and convolutional neural networks

Due to the uncertainty of the actual industrial environment, the effective features of the collected multivariate data are submerged by environmental noise. Due to the limited ability of single signal analysis method to recognize the features of multivariate data. Therefore, this paper proposes a rolling bearing fault diagnosis method based on adaptive multivariate variational mode decomposition (AMVMD) and multi-scale convolutional neural network (Multi-scale CNN). Firstly, based on the multivariate variational modal algorithm, the minimum modal overlapping component (MMOC) index is proposed and used as the objective function to seek the optimal solution of the main parameters of multivariate variational modes, to realize the adaptive decomposition and noise reduction of the original signal. Then, the multi-scale convolutional neural network was used to extract and recognize the denoising feature vectors in a deeper level, and finally the bearing fault diagnosis under complex working conditions is realized. Bearing data from Paderborn University were used to verify the proposed method. The results show that under the same conditions, the fault diagnosis accuracy of AMVMD-MSCNNs can achieve 98.60%, which has certain advantages and practical application significance.

A novel rolling bearing fault diagnosis method based on marginal spectrum

Fault diagnosis is an important method for maintaining the stable and safe running state of mechanical equipment. As most mechanical equipment faults are induced by the bearing assembly, bearing fault diagnosis is of considerable importance. At present, the mainstream intelligent diagnostic techniques include supervised learning and unsupervised learning. Supervised learning requires manual labeling and data classification, which is unfavorable for massive data amounts. Therefore, how to effectively use labeled data to increase the accuracy of diagnosis is critical, especially when the bearing failure cannot be labeled at the very beginning. This paper proposes a time–frequency analysis of the short-time Fourier transform and wavelet transform methods based on unsupervised learning. The time axis was integrated to obtain the marginal frequency of two frequency domains as a diagnostic feature, and then two clustering centroids were established automatically by the K-means of unsupervised learning. The signals were divided into two classes based on the nearest clustering centroid as the criteria for diagnosis. Finally, other bearings in different positions were classified and diagnosed using the nearest clustering centroid in the same experiment to verify the effectiveness of the method proposed in this study.