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

Rolling bearings are vital components in rotary machines where even minor faults can lead to the collapse of the entire mechanical system, resulting in significant economic losses. The condition monitoring and fault diagnosis of rolling bearings are crucial for safe operation of complex industrial systems. Traditional fault diagnosis methods mainly depend on expert experience, which leads to obvious deficiencies in diagnostic efficiency, especially in terms of accuracy. To address the issues of traditional bearing fault diagnosis relying on expert experience and low efficiency and accuracy of fault diagnosis, a bearing fault diagnosis model combining synchrosqueezed wavelet transform (SWT) and SE-ResNet is proposed. Firstly, the one-dimensional non-stationary bearing vibration signal is converted into a high-frequency two-dimensional time-frequency image through SWT, which serves as the input for the convolutional neural network; Secondly, an attention mechanism is introduced to construct an SE-ResNet model for extracting two-dimensional image features, which enhances the attention of important features and thus enhances the network's representation ability; Finally, the results of fault diagnosis are output through Softmax layer. To validate the capacity of the proposed model, experiments with the bearing dataset collected from Case Western Reserve University (CWRU) were conducted. The results show that the transfer learning accuracy of the CWRU dataset is 86.25% to 99%. This method has good feasibility, good convergence performance, and has high practical application value.

With the automation and complexity of industry, the production planning and scheduling of the smart factory depends on the proper functioning of each part. As a key component of rotating machinery, the faults of rolling bearing is prone to effects under complex and changing working conditions and coupled with other components. Therefore, it is necessary to carry out efficient and accurate fault diagnosis for rolling bearings to ensure the reliability and stability of mechanical equipment operation. This paper introduces a multi-scale rolling bearing fault diagnosis method (MSF-CNN) based on dilated convolution which is able to automatically capture complementary data and feature information of different scales of the original vibration signal through multi-scale convolution to achieve end-to-end fault diagnosis. The experimental results show that the proposed fault diagnosis method achieves an average accuracy of 99.35% under three load conditions. In addition, this method can provide the cross domain data solution to reduce the dependence on missing label data, which achieves the fault diagnosis accuracy of 89+%. It verifies the validity of our proposal.

Fault diagnosis of rolling bearings is significant for the safe operation of engineering equipment. Many intelligent diagnostic methods have been successfully developed. However, it is often susceptible to noisy environments. Therefore, the paper proposes a rolling bearing fault diagnosis method based on multimodal information fusion in time and time–frequency domains by combining an improved 1D-convolutional neural network (CNN) with ResNet50 wavelet improved CNN-ResNet (WCNN-RSN). The algorithm employs the Multi-Head Attention (MHA) mechanism to complementarily fuse fault features in different scales, achieving fault diagnosis by fully extracting fault features. The experimental results show that the diagnostic effect of WCNN-RSN is better than that of the comparison methods under noise interference, which proves that the proposed method possesses good anti-noise and generalization ability.

To realize accurate fault diagnosis of rolling bearing under random noise, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and optimized Elman_AdaBoost is proposed in this paper. First, the EEMD method is used to decompose the original vibration signal into several intrinsic mode functions (IMFs). Then, the correlation coefficient and kurtosis of IMFs are used to remove any excess ingredients, and the signal is reconstructed by using the rest of IMFs. In addition, based on the analysis of the time–frequency characteristics of the reconstructed signal, the root-mean-square value and the power spectrum center are extracted as a 2-D feature vector from each reconstructed signal and regarded as the input characteristic vector of the Elman neural network. Combined with the AdaBoost classification algorithm, the Elman_AdaBoost algorithm is improved and proposed to establish the strong classifier. Finally, based on the vibration data recorded in the bearing center of the Case Western Reserve University, three kinds of bearings with different faults and normal bearings are selected as the sample data and the training samples and test samples are constructed. Experimental results show that the proposed EEMD method and optimized Elman_AdaBoost model can effectively diagnose the rolling bearing under random noise, and has the advantages of fast speed, small error, and stable performance. Comparing with the traditional Elman_AdaBoost and single Elman neural network algorithm, this paper with fewer characteristics can get better accuracy and real-time processing performance, and presents a simple and practical resolution for the fault diagnosis of rolling bearings.

To address the challenge of low diagnostic accuracy in rolling bearing fault diagnosis under varying operating conditions, this paper proposes a novel method integrating the synchronized wavelet transform (SWT) with an enhanced Vision Transformer architecture, referred to as ResCAA-ViT. The SWT is first applied to process raw vibration signals, generating high-resolution time-frequency maps as input for the network model. By compressing and reordering wavelet transform coefficients in the frequency domain, the SWT enhances time-frequency resolution, enabling the clear capture of instantaneous changes and local features in the signals. Transfer learning further leverages pre-trained ResNet50 parameters to initialize the convolutional and residual layers of the ResCAA-ViT model, facilitating efficient feature extraction. The extracted features are processed by a dual-branch architecture: the left branch employs a residual network module with a CAA attention mechanism, improving sensitivity to critical fault characteristics through strip convolution and adaptive channel weighting. The right branch utilizes a Vision Transformer to capture global features via the self-attention mechanism. The outputs of both branches are fused through addition, and the diagnostic results are obtained using a Softmax classifier. This hybrid architecture combines the strengths of convolutional neural networks and Transformers while leveraging the CAA attention mechanism to enhance feature representation, resulting in robust fault diagnosis. To further enhance generalization, the model combines cross-entropy and mean squared error loss functions. The experimental results show that the proposed method achieves average accuracy rates of 99.96% and 96.51% under constant and varying load conditions, respectively, on the Case Western Reserve University bearing fault dataset, outperforming other methods. Additionally, it achieves an average diagnostic accuracy of 99.25% on a real-world dataset of generator non-drive end bearings in wind turbines, surpassing competing approaches. These findings highlight the effectiveness of the SWT and ResCAA-ViT-based approach in addressing complex variations in operating conditions, demonstrating its significant practical applicability.

Traditional transformer-based fault diagnosis methods exhibit inherent limitations in extracting local features, making it challenging to effectively identify critical fault characteristics when processing rolling bearing vibration signals, especially in high-noise environments. Therefore, this paper proposes an intelligent diagnosis method based on noise learning, called the multi-scale residual convolution one-dimensional vision transformer (MRC-1DViT). Firstly, the MRC module is constructed using an adaptive noise learning approach to extract local fault features across multiple scales, capturing fault information of varying dimensions, and reduce noise interference. Subsequently, the extracted features are fed into a 1DViT based fault diagnosis model to identify the long-distance dependencies between features. Additionally, contra norm is introduced to mitigate the issue of dimensional collapse in the model. Finally, the effectiveness of MRC-1DViT is validated using the Case Western Reserve University (CWRU) rolling bearing dataset and the Southeast University (SEU) gearbox dataset. Experimental results demonstrate that MRC-1DViT exhibits superior noise robustness and higher stability compared to existing methods. Notably, under a high-noise environment of −6 dB signal-to-noise ratio, the proposed method achieves fault diagnosis accuracies of 91.38% and 89.38%, respectively.

In recent years, many diagnosis methods in few-shot fault diagnosis have achieved remarkable results on known faults with limited samples. But in actual industrial conditions, new fault types often emerge during long-term equipment operation. These methods require retraining with new samples, thus failing to meet rapid diagnosis needs. To address this, this paper proposes MAML-CNN-GRU, a novel few-shot model based on the Model-Agnostic Meta-Learning (MAML) framework. Firstly, the CNN-GRU hybrid model can effectively extract the spatiotemporal features from original time-series fault signals. Specifically, it uses CNN to extract local spatial features and GRU to capture long-term sequence dependencies. Secondly, leveraging MAML's meta-training mechanism, the initial parameters of CNN-GRU can be optimized through multiple fault diagnosis tasks. As a result, it gains the ability to learn cross-task general features. Then, a reasonable meta-task generation strategy enables rapid identification of novel fault types or variable condition faults even with limited samples. Ultimately, results from CWRU and XJTU bearing datasets illustrate this method’s outstanding performance in diagnosing novel faults under diverse operating conditions. For unknown or compound faults, this method achieves 98.91% and 96.96% diagnosis accuracy in 3-way 5-shot and 5-way 5-shot tasks respectively. Evidently, these results validate the method’s effectiveness.

Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the fine-to-coarse multiscale permutation entropy (F2CMPE), Laplacian score (LS) and support vector machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on the wavelet packet decomposition. The entropy measure estimates the complexity of time series from both low- and high-frequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional composite multiscale permutation entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification, respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify the bearing fault patterns under different fault states and severity levels.

Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.

Many nonlinear dynamic and statistic methods, including multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), have been widely studied and employed to fault diagnosis of the rolling bearing. Multiscale dispersion entropy (MDE) is a powerful tool for complexity measure of time series, and compared with MSE and MFE, it gets much better performance and costs less time for computation. Since single-channel time series analysis will cause information missing, inspired by multivariate multiscale sample entropy (MMSE) and multivariate multiscale fuzzy entropy (MMFE), refined composite multivariate multiscale dispersion entropy (RCMMDE) was proposed in this paper. After that, RCMMDE was compared with MDE, MMSE, and MMFE by analyzing synthetic signals and the results show that the RCMMDE has certain advantages in terms of robustness. A hybrid fault diagnostics approach is proposed for rolling bearing with a combination of RCMMDE, multi-cluster feature selection, and support vector machine. Also, the proposed method is compared with MDE, MMSE, and MMFE, as well as multivariate multiscale dispersion entropy-based fault diagnosis methods by analyzing the experimental data of rolling bearing, and the result shows that the proposed method gets a higher identification rate than the existing other fault diagnosis methods.

The rolling bearing fault signals are accompanied with the specific periodic impulse components in case of fault. Due to the background noise, the periodic impulse component is extremely weak and difficult to be extracted. The periodic segment matrix (PSM) has excellent singular value aggregation property and can accurately separate the periodic impulse components from the signal. Based on this, a symplectic period mode decomposition (SPMD) method is proposed. Firstly, the singular value squared difference ratio (SVSDR) spectrum is defined, and the embedded dimension of PSM is determined. Then, the symplectic geometry component matrix is obtained by symplectic geometry similarity transformation, and the first symplectic geometry period component (SPC) is obtained by periodic impact intensity (PII). Finally, the spectral L2/L1 norm (SNL2/L1) index is used as the termination condition, and the iterative decomposition is terminated when the SNL2/L1 of the residual signal decreases. The emulation and experimental signals results indicate that the SPMD method can extract periodic impulse components accurately and is an effective rolling bearing fault diagnosis method.

Rolling bearings are important components widely used in mechanical equipment. To ensure the healthy operation of various machinery, this paper proposes a fault diagnosis method for rolling bearings based on bearing stress wave data, integrating Gramian Angular Fields (GAFs), Markov Transition Fields (MTF), and a deep residual network (ResNet). This method addresses the issue of traditional fault diagnosis methods based on vibration signals, which may have a single extracted feature or low diagnostic accuracy. The method first transforms one-dimensional bearing stress wave data into a two-dimensional image. Then, the two-dimensional image is fed into the deep residual network (ResNet). Utilizing the network's advantages in image classification and recognition, the method achieves classification of different types of bearing faults. Experimental results show that this method has better accuracy compared to traditional bearing fault diagnosis models.

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

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.

Limited operating data resulting from complex and changeable working conditions significantly undermines the performance of deep learning-based methods for rolling bearing fault diagnosis. Generally, this problem can be solved by using the generative adversarial network (GAN) to augment data. However, most GAN-based methods seldom comprehensively consider the global interactions and local dependencies in raw vibration signals during data generation, leading to a decline in the quality of the generated data and compromising the diagnostic accuracy. To address this problem, a fault diagnosis method based on integrated convolutional transformer GAN (ICoT-GAN) is proposed to improve the diagnostic performance under limited data condition by generating high-quality signals. Firstly, a new data augmentation model ICoT-GAN is developed. In ICoT-GAN, a novel ICoT block is designed to construct the generator and discriminator. The ICoT block achieves the integration of attention-based global information capture and convolution-based local feature extraction through the incorporation of convolution within the transformer encoder. This design allows the ICoT-GAN to comprehensively extract both global and local time-series features of raw signals and generate high-quality signals. Secondly, a novel data evaluation indicator, referred to as the Multiple Time-Domain Features Indicator (MTFI), is designed to quantitatively evaluate generated signals’ quality by calculating the similarity of time-domain features between the real and generated signals. The MTFI can complement probability distribution indicators and provide a comprehensive evaluation of the data augmentation model’s generation ability by considering both time-series feature similarity and probability distribution differences. Finally, the effectiveness of our proposed method has been successfully demonstrated under limited data condition using the CWRU and IMS bearing datasets. With only 16 training samples per class, our proposed method achieves diagnostic accuracy of 99.99% and 99.70%, respectively. Additionally, the data generation time is only 1163.31s, indicating the efficiency of our proposed method.